energies Article Method of Geomechanical Parameter Determination and Volumetric Fracturing Factor Simulation under Highly Stochastic Geologic Conditions Dongmei Ding 1 , Yongbin Wu 2, *, Xueling Xia 1 , Weina Li 1 , Jipeng Zhang 3 and Pengcheng Liu 3 1 2 3 * Beijing Sunshine Geo-Tech Co., Ltd., Beijing 100192, China Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China School of Energy Resources, China University of Geosciences, Beijing 100083, China Correspondence: wuyongbin@petrochina.com.cn Abstract: In order to accurately predict geomechanical parameters of oil-bearing reservoirs and influencing factors of volumetric fracturing, a new method of geomechanical parameter prediction combining seismic inversion, well logging interpretation and production data is proposed in this paper. Herein, we present a structure model, petrophysical model and geomechanical model. Moreover, a three-dimensional geomechanical model of a typical reservoir was established and corrected using history matching. On this basis, a typical well model was established, 11 influencing factors of volume fracturing including formation parameters and fracturing parameters were analyzed and their impact were ranked, and the oil recovery rate and the accumulated oil production before and after optimal fracturing were compared. The results show that with respect to formation parameters, reservoir thickness is the main influencing factor; interlayer thickness and stress difference are the secondary influencing factors; and formation permeability, Young’s modulus and Poisson’s ratio are the weak influencing factors. For a pilot well of a typical reservoir, the optimized fracture increased production by 7 tons/day relative to traditional fracturing. After one year of production, the method increased production by 4 tons/day relative to traditional fracturing, showing great potential in similar oil reservoirs. Citation: Ding, D.; Wu, Y.; Xia, X.; Li, W.; Zhang, J.; Liu, P. Method of Keywords: volumetric fracturing; low-permeability reservoir; geomechanical parameters; oil recovery Geomechanical Parameter Determination and Volumetric Fracturing Factor Simulation under Highly Stochastic Geologic Conditions. Energies 2023, 16, 312. https://doi.org/10.3390/en16010312 Academic Editor: Reza Rezaee Received: 26 November 2022 Revised: 23 December 2022 Accepted: 24 December 2022 Published: 27 December 2022 Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1. Introduction Volumetric fracturing has become the primary stimulation method for oil recovery from low-to-tight permeability reservoirs in recent years. For reservoirs with extensive existing wells, the geologic parameters are relatively deterministic, and the operation parameters of volumetric fracturing can be obtained based on existing geomechanical information. In contrast, for new reservoirs with few or no existing wells, the spatial distribution of geologic and geomechanical parameters is highly stochastic, and drilling and volumetric fracturing are highly risky [1–4]. According to PetroChina statistics, the past ratio of economic production in newly developed tight oil reservoirs with volumetric fractured wells is less than 40%. It is increasingly realized that a new method should be proposed on the basis of the traditional method of geomechanical parameter determination based purely on well logging data to massively improve the success ratio of volumetric fracturing. Traditionally, geomechanical parameters were obtained from the correlation functions of the geomechanical parameters with logging parameters, such as Young’s modulus and Poisson ratio, with gamma and acoustic logging data. This method has detrimental disadvantages, as logging data are only a reflection of the rock parameters in the wellbore or the well vicinity region. Whereas volumetric fracture development is considerably affected by the rock lithology, petrophysical parameters induce heterogeneous geomechanical parameters in the region far from the wellbore [5–8]. Energies 2023, 16, 312. https://doi.org/10.3390/en16010312 https://www.mdpi.com/journal/energies Energies 2023, 16, 312 2 of 20 Under highly uncertain geologic conditions or in reservoirs with few or no wells, the petrophysical properties interpreted from well logging data are in poor agreement with the real spatial distribution of these parameters. In order to improve the success ratio of geomechanical parameter prediction, researchers have proposed various methods that have been applied in the field with both success and failure. It has been found that the accurate prediction of geomechanical parameters is a prerequisite for successful volumetric fracturing operation. The use of an ensemble long short-term memory (EnLSTM) network in well log generation [9] is one such method for predicting geomechanical parameters based on well logging interpretation. Researchers trained an interpretation model based on an available data set and combined an ensemble neural network and a cascaded LSTM network to improve the model accuracy in interpretation. In order to deal with the issues of overconvergence and disturbance compensation, two methods were applied, which improved the accuracy in interpreting the geomechanical properties from well logging data. Geomechanical properties can also be obtained from drilling data [4,10,11]. A costeffective technology was proposed that uses commonly available drilling data to deduce the geomechanical properties of rock without the need for downhole logging operations and interpretation. In order to accurately calculate the friction parameters of the wellbore and the downhole weight on a bit, a new wellbore friction model was built and validated using field data. Based on this, the formation lithology constants for different rock types were used to assist in calculating the geomechanical properties of reservoir formations, including confined compressive strength (CCS), unconfined compressive strength (UCS), Young’s modulus, permeability, porosity and Poisson’s ratio. Artificial intelligence neural networks, data mining, machine learning techniques and deep learning have also been introduced to estimate dynamic geomechanical properties, including Poisson’s ratio, Young’s modulus and Lamé parameters [12–17]. Furthermore, the application of core and log data is also used to predict rock mechanics. For example, porosity can be employed as a geomechanical index to enable the estimation of rock mechanic material properties using general and field-specific correlations [18]. Sequence stratigraphy and geomechanics also have some correlations, as many geological properties affect geomechanical properties and, ultimately, reservoir operations and performance [19]. Petroelastic and geomechanical classification of lithologic facies also have correlations to some extent, representing a new research frontier [20]; through the analysis of rock facies and rock properties, the correlations between petroelastic and geomechanical properties can be determined. Principal stresses, including the vertical, maximum horizontal and minimum horizontal stresses, and elastic moduli related to rock brittleness, such as Young’s modulus and Poisson’s ratio, can also be estimated from wide-angle, wide-azimuth 3D seismic data, which were used to optimize the placement and direction of horizontal wells and hydraulic fracture stimulations [21]. The combination of well logs and seismic reflection data to predict geomechanical data is a new trend [22]. Researchers investigated the wireline log data of four wells and regional seismic reflection data and establish a workflow for accurate estimation of geomechanical parameters; this process was validated by field volumetric fracturing parameter optimization and successful implementations. Investigators found that the precise estimation of reservoir geomechanical parameters using this method can reduce risk and provide benefits throughout the lifespan of an oil and gas field. In this work, we propose a new geomechanical parameter prediction method coupling both seismic reversion and well logging interpretation data, based on which a typical well model was built, the influence factors of volumetric fracturing were simulated and their impacts were determined. Finally, a typical pilot test well was designed, fracturing parameters were optimized and the production before and after fracturing optimization was predicted and compared, which validated the feasibility and accuracy of the methodology proposed in this study. Energies 2023, 16, 312 3 of 20 2. Geomechanical Property Modeling Method Coupling Logging and Seismic Reflection Data At present, commonly used modeling methods include deterministic modeling, stochastic modeling, etc. For the parameters that can be determined, the deterministic modeling method is preferred. For parameters with many influencing factors, the method of combining deterministic modeling with stochastic modeling is considered. Especially when random modeling is used, seismic reflection data are used to constrain the well so as to reduce the uncertainty of the model and improve the accuracy of the 3D geological model. The target geological model presented in this study is set up mainly adopting the method of combining deterministic modeling and stochastic modeling, making full use of the characteristics of logging data with high vertical resolution, seismic data and the second variable involved in the geological model of simulation calculation. In areas with multiple wells, deterministic modeling is performed on the basis of well data, with reference to earthquake information. In areas without wells, seismic information is mainly used for simulation operation, not only making full use of well data but also overcoming the difficulty of controlling the structure and reservoir due to the low drilling density, enhancing the reliability of the established geological model. Moreover, for well sections with information about already fractured wells, history matching is used to further calibrate the geomechanical parameters that affect the volumetric fracturing result and the production performance. The overall workflow used to extract a geomechanical model from existing geologic, seismic, fracturing and production information is shown in Figure 1. The major difference from traditional modeling is that the seismic reflection data are used to constrain the determined well logging interpretation and stochastic lithofacies; structure and petrophysical modeling are used to ensure reasonable spatial distribution of the structure, lithofacies and petrophysical properties; and the fracturing and production dynamic data are used to further calibrate the model properties mentioned above. Moreover, the fracturing and Energies 2023, 16, x FOR PEER REVIEW 4 of 21 production dynamic data are used in combination with the geomechanical functions to extract the 3D geomechanical model. Logging interpretation Well-seismic correlation Structure interpretation Geologic data Structure model Lithofacies model Seismic inversion body constraint Porosity model Petrophysical model Permeability model Saturation model Model quality analysis 3D Structure model 3D Lithofacies model Net/gross ratio model Fracturing & production history match 3D petrophysical model Fracturing & production history match 3D geomechanical model Figure 1. 1. The The overall overall workflow workflow used used to to extract extract geomechanical geomechanical model. model. Figure The The specific specific method method and and steps steps are are as as follows: follows: (1) (1) Establish a high-precision 3D structure model Based on the fault data, layer data and single-well geological stratification data provided by seismic interpretation, in combination with reservoir development characteristics, formation thickness distribution variation under a tectonic background and the contact relationship between each sublayer, reasonable mesh division and modeling methods are adopted to establish a high-precision 3D structure model. Then, 3D seismic data and Energies 2023, 16, 312 4 of 20 Based on the fault data, layer data and single-well geological stratification data provided by seismic interpretation, in combination with reservoir development characteristics, formation thickness distribution variation under a tectonic background and the contact relationship between each sublayer, reasonable mesh division and modeling methods are adopted to establish a high-precision 3D structure model. Then, 3D seismic data and drilling data are used to check and control the structural model. (2) Establish a 3D petrophysical model characterizing lithofacies and reservoir parameters Figure 2 shows the workflow of a fine 3D petrophysical model. Because it is difficult to describe the spatial distribution of the reservoir parameters with interwell interpolation alone, the logging interpretation results of reservoir sandstone, tight sandstone and mudstone are used in single-well lithoface division and inversion body correlation analysis. According to the analysis result, the inversion body is adopted as a constraint control to improve the lithoface model. Under the constraint of the lithoface model, taking porosity, permeability and oil saturation of logging interpretation as input data, in combination with Energies 2023, 16, x FOR PEER REVIEW 5 of 21 the seismic inversion body of reservoir parameters, using geostatistics and the lithofacecontrolled random simulation method, fine reservoir parameter models are established. Logging interpretation Structure interpretation Well-seismic correlation Geologic data Fault model Structure model Layer model Lithofacies model Seismic inversion body constraint Payzone model Petrophysical model Porosity model Permeability model Saturation model Model quality analysis Net/gross ratio model Fracturing & production history match 3D petrophysical+Lithofacies model Figure 2. 2. Workflow Workflow of of the the fine fine3D 3Dpetrophysical petrophysicalmodel. model. Figure (3) (3) Establish a 3D geological model characterizing geomechanical parameters shows the the workflow workflow of of the the fine fine 3D 3D geomechanical geomechanical model. model. The 3D geomeFigure 3 shows chanical model modelhas hasmultiple multiple properties, including overburden pressure, payzone pore chanical properties, including overburden pressure, payzone pore prespressure, maximum and minimum horizontal principal stress, compressive uniaxial compressive sure, maximum and minimum horizontal principal stress, uniaxial strength, strength,modulus, Young’s modulus, ratio and brittleness index. A modified method was Young’s Poisson’s Poisson’s ratio and brittleness index. A modified method was adopted in building geomechanical model distinct from the traditional single-wellsingle-well geomechanical adopted in abuilding a geomechanical model distinct from the traditional geomodel. It should beItnoted that seismic inversion structure model mechanical model. should be the noted that the seismicbody-constrained inversion body-constrained strucand model weremodel used for spatial of the geomechanical model, ture lithoface model and lithoface were used characterization for spatial characterization of the geomechanand historyand matching existing fracturing and production dynamic data was data also ical model, historyusing matching using existing fracturing and production dynamic necessary for further calibration of the geomechanical model. was also necessary for further calibration of the geomechanical model. Geologic model Parameters Overburden pressure Structure model (Seismic inversion body constrained) Payzone pore pressure Constraint method Volume density integral Pressure gradient method Horizontal minimum principal stress Horizontal maximum principal stress Uniaxial compressive strength Young’s modulus Effective stress ratio Effective stress ratio Lithofacies control History match Lithofacies control 3D geomechanic model Overburden pressure Payzone pore pressure Horizontal minimum principal stress Horizontal maximum principal stress Uniaxial compressive strength Young’s modulus Energies 2023, 16, 312 pressure, maximum and minimum horizontal principal stress, uniaxial compressive strength, Young’s modulus, Poisson’s ratio and brittleness index. A modified method was adopted in building a geomechanical model distinct from the traditional single-well geomechanical model. It should be noted that the seismic inversion body-constrained structure model and lithoface model were used for spatial characterization of the geomechanof 20 ical model, and history matching using existing fracturing and production dynamic5 data was also necessary for further calibration of the geomechanical model. Geologic model Parameters Overburden pressure Structure model (Seismic inversion body constrained) Payzone pore pressure Constraint method Volume density integral Pressure gradient method Horizontal minimum principal stress Horizontal maximum principal stress Uniaxial compressive strength Young’s modulus Lithofacies model (Seismic inversion body constrained) Poisson ratio Brittleness index Effective stress ratio Effective stress ratio Lithofacies control History match Lithofacies control History match Lithofacies control History match Young’s modulus Poisson ratio 3D geomechanic model Overburden pressure Payzone pore pressure Horizontal minimum principal stress Horizontal maximum principal stress Uniaxial compressive strength Young’s modulus Poisson ratio Brittleness index Figure Figure 3. 3. Workflow Workflowof ofthe thefine fine3D 3Dgeomechanical geomechanicalmodel. model. 3. Geomechanical Property Modeling for a Typical Reservoir 3. Geomechanical Property Modeling for a Typical Reservoir 3.1. Overburden Pressure 3.1. Overburden Pressure Figure 4 shows the overburden pressure modeling workflow. As shown in Figure 4a–c, Figure 4 shows the overburden pressure According modeling workflow. As shown in Figure the overburden pressure is density-integral. to the density curve from the Energies 2023, 16, x FOR PEER REVIEW 6 of 21 4a–c, the overburden pressure is density-integral. According to the density curve from ground to the bottom of the well fitted by a single well, density modeling is carried outthe in ground to the bottom of the well fitted by grid a single well, density modeling is carried out in combination with the 3D geological model to obtain the density body from the ground combination withinthe geological grid to obtain the density body pressure from the to the target layer the3D whole region; model then, the three-dimensional overburden ground to theistarget layerbyinintegral. the whole the three-dimensional overburden pressure body obtained density integral. Thethen, pressure distribution of overlybody is obtained by density Theregion; pressure distribution range ofrange overlying strata ing strataformation in target formation is 32MPa. MPa–40 MPa. The parameter maps and cross-section in target is 32 MPa–40 The parameter maps and cross-section maps show maps show that the of thestrata overlying strata is mainly affected bydepth the buried depth that the pressure of pressure the overlying is mainly affected by the buried of strata and of strata and increases gradually from north to south. increases gradually from north to south. (a) Figure Figure4.4. Overburden Overburdenpressure pressuremodeling modelingworkflow. workflow.(a) (a)Single-well Single-welldensity densitycurve; curve;(b) (b)3D 3Ddensity density model; model;(c) (c)3D 3Doverburden overburdenpressure pressure model. model. 3.2.Three-Dimensional Three-DimensionalPore PorePressure Pressure 3.2. The basic principle of porepressure pressure prediction prediction isis under-compaction under-compactiontheory. theory. Under Under The basic principle of pore normalcircumstances, circumstances,the thestratum stratumisisgradually graduallycompacted compactedand anddiagenetic diageneticunder underthe theprespresnormal sureof ofthe theoverlying overlyingstratum. stratum.The Thefluid fluidin inthe thepores poresofofthe thestratum stratumisisgradually graduallydischarged discharged sure duringthe thecompaction compactionprocess, process,and andthe thepressure pressureof ofthe theoverlying overlyingstratum stratumisismainly mainlyborne borne during by the rock skeleton. The pore fluid only carries hydrostatic pressure in the pores, and the by the rock skeleton. The pore fluid only carries hydrostatic pressure in the pores, and the formation porosity decreases exponentially with increasing depth. formation porosity decreases exponentially with increasing depth. If depth is linear and porosity is logarithmic, the depth–porosity curve is a straight line. Because the sonic velocity of the formation is linear to porosity, the velocity curve or sonic time difference curve is also a straight line, representing normal compaction. When the sedimentary stratum is a large set of mudstone or sand–mud mixed sedimentation, the stratum permeability is very low. During the compaction diagenesis process, the stratum fluid (mainly water) cannot be discharged, and the pore fluid not only Energies 2023, 16, 312 6 of 20 If depth is linear and porosity is logarithmic, the depth–porosity curve is a straight line. Because the sonic velocity of the formation is linear to porosity, the velocity curve or sonic time difference curve is also a straight line, representing normal compaction. When the sedimentary stratum is a large set of mudstone or sand–mud mixed sedimentation, the stratum permeability is very low. During the compaction diagenesis process, the stratum fluid (mainly water) cannot be discharged, and the pore fluid not only bears hydrostatic pressure but also a part of the overlying stratum pressure, so high pressure occurs. Because the pore fluid carries part of the pressure of the overlying strata, the pressure of the rock skeleton is reduced and therefore cannot be fully compacted, retaining high pore pressure, which is often referred to as “under-compacted”. Pressure anomalies due to under-compaction preserve higher porosity and lower sonic or seismic velocities. This characteristic of the under-pressed field stratum is the theoretical basis of pressure prediction, that is, the pore pressure can be predicted by using the low-velocity anomaly of velocity. Therefore, the Eaton method is used to forecast the pore pressure, as expressed by the following equation [23]. Pp = Sv − (Sv − Ph ) tn to N (1) where Pp refers to the predicted payzone pore pressure (MPa), Sv is the overburden pore pressure (MPa), Ph is the hydrostatic pressure (MPa); tn is the reciprocal of seismic velocity under normal compaction (µs/ft); to is the reciprocal of measured seismic velocity (µs/ft) and N is the Eaton index. Energies 2023, 16, x FOR PEER REVIEW of 21 Figure 5 shows the cross-section map of the pore pressure coefficient in the7 N–S direction. As shown in Figure 5, due to the influence of stratum burial depth, the pore pressure of the southern formation is higher, but the change in pore pressure coefficient is minimal, minimal, ranging ranging from from 1.25 1.25 to to 1.50 1.50 on on the the whole, whole, with with an average average of approximately 1.30. The The pore pore pressure pressureof ofthe thetarget targetstratum stratumof of the the target target formation formation in in this this study study is is distributed distributed in in the the range range of of 18–26 18–26 MPa. MPa. As As can can be be seen seen from from the the cross-section cross-section map map of of pore pore pressure pressure in in the the whole whole area, area, there there is is little little difference differencein in the the values values of of sand sand and and mudstone, mudstone, and and the the main main distribution distribution range range is is 20–23 20–23 MPa. MPa. Figure 5. 5. Cross-section Cross-section map map of of the the pore porepressure pressurecoefficient coefficientin inthe theN–S N–Sdirection. direction. Figure 3.3. Three-Dimensional Horizontal Horizontal Principal Principal Stress Stress 3.3 Three-Dimensional Three-dimensional horizontal principal Three-dimensional horizontal principal stress stress is is calculated calculated by by the the combined combined spring spring model. model. Conventional Conventional logging logging and and drilling drilling mud mud data data are are absent absent from from imaging imaging logging logging data data in the process of determining the structural coefficient. Therefore, determination of in the process of determining the structural coefficient. Therefore, determination of the the structural refers to to the the rock rock failure failuretest testofofonly onlyone onewell wellininthis this area and that structural coefficient coefficient refers area and that of of adjacent area, taking = 0.0001, coefficient = to 0.8calculate to calculate y = 0.0005 thethe adjacent area, taking εx ε=x0.0001, εy =ε0.0005 andand BiotBiot coefficient = 0.8 the minimum and maximum horizontal principal stress in the whole area. The following equations are used to calculate the maximum and minimum horizontal principal stress [24]. π π π£ 1 − 2π£ πΈ π£πΈ π + πΌπ + π + π 1−π£ 1−π£ 1−π£ 1−π£ π£ 1 − 2π£ πΈ π£πΈ = π + πΌπ + π + π 1−π£ 1−π£ 1−π£ 1−π£ = (2) (3) 3.3 Three-Dimensional Horizontal Principal Stress Energies 2023, 16, 312 Three-dimensional horizontal principal stress is calculated by the combined spring model. Conventional logging and drilling mud data are absent from imaging logging data in the process of determining the structural coefficient. Therefore, determination of the structural coefficient refers to the rock failure test of only one well in this area and that 7 of of 20 the adjacent area, taking εx = 0.0001, εy = 0.0005 and Biot coefficient = 0.8 to calculate the minimum and maximum horizontal principal stress in the whole area. The following equations are used to calculate the maximum and minimum horizontal principal stress the minimum and maximum horizontal principal stress in the whole area. The following [24]. equations are used to calculate the maximum and minimum horizontal principal stress [24]. π£ 1 − 2π£ πΈ π£πΈ (2) π = v π +1 − 2v πΌπ + E π + vE π (2) Shmin = 1 − π£Sv + 1 − π£αPp + 1 − π£2 ε x + 1 − π£2 ε y 1 −π£v 11− 1 −πΈv 1 −π£πΈ v −v2π£ (3) = π + πΌπ + π + π π 1v− π£ 1E −π£ 1vE −π£ 11 −−2vπ£ Shmax = Sv + αPp + εy + εx (3) −v 1principal −v 1 − (MPa), v2 − vis2 the horizontal maxwhere Shmin is the horizontal1 minimum stress S1hmax imum S principal stress (MPa),minimum v is the Poisson ratio (f), E is theSYoung’s modulus (MPa), α where horizontal principal stress (MPa), horizontal maxihmin is the hmax is the is the principal Biot coefficient (f), Sv visisthe (MPa) and modulus Pp is the pore pressure mum stress (MPa), theoverburden Poisson ratiopressure (f), E is the Young’s (MPa), α is the (MPa). Biot coefficient (f), Sv is the overburden pressure (MPa) and Pp is the pore pressure (MPa). Figure 66 shows minimum horizontal principal stress in the showsthe thecross-section cross-sectionmap mapofof minimum horizontal principal stress in the N–S direction. As shown in Figure the minimum horizontal principal stress inN–S direction. As shown in Figure 6, the6,minimum horizontal principal stress increases creases gradually fromtonorth south andtop from top to bottom, a distribution range of gradually from north southtoand from to bottom, with a with distribution range of 25–32 25–32 MPa and an average of 27.6 MPa. The plane changes gradually, which is conducive MPa and an average of 27.6 MPa. The plane changes gradually, which is conducive to the to the uniform propagation of fractures. uniform propagation of fractures. Energies 2023, 16, x FOR PEER REVIEW 8 of 21 6. Cross-section map of minimum horizontal principal stress in the N–S direction. Figure direction. 3.4 Three-Dimensional Horizontal Stress Difference 3.4. Three-Dimensional Horizontal Stressmap Difference Figure 7 shows the cross-section of horizontal principal stress difference in the N–S Figure direction. As shown in Figure 7, the horizontal stress difference be calculated 7 shows the cross-section map of horizontal principal stresscan difference in the using the maximum horizontal principal stress and the minimum horizontal principal N–S direction. As shown in Figure 7, the horizontal stress difference can be calculated using stress. The horizontal stress difference the the areaminimum is between 2 and 10principal MPa, with an averthe maximum horizontal principal stressinand horizontal stress. The age of 5.7 MPa. distribution horizontal horizontal stressThe difference in the characteristics area is betweenof 2 and 10 MPa,stress withdifference an averagewere of 5.7statisMPa. tically analyzed according to lithology categories; mudstone horizontal stress differThe distribution characteristics of horizontal stressthe difference were statistically analyzed ence is slightly lower categories; than sandstone horizontal stress difference becauseisthe sand maxiaccording to lithology the mudstone horizontal stress difference slightly lower mum horizontal principal stress is close to that of mudstone, whereas the minimum horithan sandstone horizontal stress difference because the sand maximum horizontal principal zontalisprincipal of mudstone, sandstone iswhereas slightlythe lower than that of mudstone. The main stress close tostress that of minimum horizontal principal stressdisof tribution range of horizontal stress difference is conducive to the formation complex sandstone is slightly lower than that of mudstone. The main distribution range of of ahorizontal stress difference is conducive to the formation of a complex fracture network. fracture network. Figure 7. Cross-section map of horizontal principal stress stress difference difference in in the the N–S N–S direction. direction. 3.5 Three-Dimensional Rock Geomechanical Parameters Lithology directly affects the uniaxial compressive strength, Young’s modulus, Poisson’s ratio and other mechanical properties, so the face-controlled modeling method is used to establish a three-dimensional rock mechanical parameter model. In addition, the Energies 2023, 16, 312 8 of 20 3.5. Three-Dimensional Rock Geomechanical Parameters Lithology directly affects the uniaxial compressive strength, Young’s modulus, Poisson’s ratio and other mechanical properties, so the face-controlled modeling method is used to establish a three-dimensional rock mechanical parameter model. In addition, the brittleness index is the basis for the formation of fracture networks in tight reservoirs during fracturing. In this study, we also calculated the formation brittleness in the study area. The calculation method of normalized Young’s modulus and Poisson’s ratio was adopted (Rickman R, 2008). The brittleness of rock is expressed as the relationship between stress and strain in rock mechanics. Poisson’s ratio is used to characterize the relationship between the transverse strain caused by uniformly distributed longitudinal stress and the corresponding longitudinal strain. Young’s modulus is a parameter describing the relationship between longitudinal stress and longitudinal strain caused by longitudinal stress. Therefore, the Poisson’s ratio and Young’s modulus of rock elastic parameters can be used to calculate the brittleness index. The equation of calculation is as follows [25]. Figure 8 shows the cross-section map of Young’s modulus in the N–S direction. Figure 9 shows the cross-section map of Poisson’s ratio in the N–S direction. Figure 10 shows the cross-section map of the brittleness index in the N–S direction. Analysis of the calculated mechanical parameter model results shows that the distribution range of Young’s modulus, Poisson’s ratio and the brittleness index in the target formation is 15–27 GPa, 0.2–0.3 and 40–60%, respectively. BI = YM_BRIT + PR_BRIT 2 YMBRIT = Energies 2023, 16, x FOR PEER REVIEW PRBRIT = YMSC − YMmin × 100% YMmax − YMmin PRC − PRmax × 100% PRmin − PRmax (4) (5) 9 of 21 (6) where YMSC refers to Young’s modulus modulus (104 MPa); MPa); PRC PRC is is Poisson’s Poisson’s ratio ratio(dimensionless); (dimensionless); YM_BRIT is Young’s modulus after normalization (dimensionless); PR_BRIT is Poisson’s ratio after normalization (dimensionless); (dimensionless); BI BI refers refers to to the brittleness brittleness index; index; and and YMmax, YMmax, YMmin, PRmin and PRmax are the maximum and minimum values of Young’s modulus and Poisson’s ratio, respectively. respectively. Figure 8. Cross-section map of Young’s modulus modulus in in the the N–S N–S direction. direction. Energies 2023, 16, 312 9 of 20 Figure 8. Cross-section map of Young’s modulus in the N–S direction. Figure 9. 9. Cross-section Cross-section map map of of Poisson Poisson ratio ratio in in the the N–S N–S direction. direction. Figure Figure 10. 10. Cross-section Cross-section map map of of the the brittleness brittleness index index in in the the N–S N–S direction. direction. Figure Based on on the the geomechanical geomechanical modeling modeling results, results, the the geomechanical geomechanical parameters parameters were 10 ofwere 21 Based calibrated by history matching of an existing well fracturing operation with the produccalibrated by history matching of an existing well fracturing operation with the production history so as to ensure the precision of the simulation model. Figure 11 shows the tion history so as to ensure the precision of the simulation model. Figure 11 shows the production history matching result, which is in agreement with the actual field operations. production result, which is inheterogeneity agreement with actual field operations. fractures are history highly matching affected by the reservoir andthe stress changes in each Figure 12 shows the spatial fracture distribution in each payzone, which indicates that the Figure 12 shows the spatial fracture distribution in each payzone, which indicates that the zone. fractures are highly affected by the reservoir heterogeneity and stress changes in each zone. Energies 2023, 16, x FOR PEER REVIEW Figure History matching curves liquid rate and rate. Figure 11.11. History matching curves of of thethe liquid rate and oiloil rate. Energies 2023, 16, 312 10 of 20 Figure 11. History matching curves of the liquid rate and oil rate. Figure Figure12. 12.Spatial Spatialdistribution distributionof offractures fracturesunder underheterogeneous heterogeneous conditions. conditions. Thepetrophysical petrophysicaland andgeomechanical geomechanical parameters parameters of of each eachlayer layerin inthe thework workarea areawere were The calibrated by byhistory history matching, matching, providing providing a data data reference reference for subsequent sensitivity calibrated sensitivity analyanalsis ofoffracturing reservoir parameter simulation. A comparison of the obtained ysis fracturingand and reservoir parameter simulation. A comparison of results the results obusing the traditional method and those using using the method proposed in thisinstudy tained using the traditional method and obtained those obtained the method proposed this basedbased on theon statistics before before and after history matching is shownisin Table in 1, which indicates study the statistics and after history matching shown Table 1, which an obvious variance, as reflected in the second column showing traditional calculation indicates an obvious variance, as reflected in the second column showing traditional calresults. History using existing and production data is critical tocritical further culation results. matching History matching using fracturing existing fracturing and production data is calibrate geomechanical parameters, particularly in multilayer reservoirs.reservoirs. to further the calibrate the geomechanical parameters, particularly in multilayer Table 1. 1. Comparison Comparison of of geomechanical geomechanical parameters parameters before before and after calibration. Table Item Item Traditional Calculation Before History Before History Matching Traditional Calculation Minimum horizontal principal stress 22–30 MPa 25–32Matching MPa Minimum principal stress 22–30 MPa 25–32 MPa Reservoirhorizontal stress difference between 1.1–9.1 MPa 2–10 MPa payzone interlayer Reservoir stressand difference between Energies 2023, 16, x FOR PEER REVIEW 1.1–9.1MPa Young’s modulus 15.6–25.1 GPa 15–272–10 GPa MPa payzone and interlayer Poisson’s ratio 0.22–0.35 0.2–0.3 Young’s modulus 15.6–25.1 GPa 15–27 GPa Poisson’s ratio 0.22–0.35 0.2–0.3 4. Influence Influence Factors Factorson onVolumetric VolumetricFracturing FracturingPerformance Performance 4. 4.1. 4.1. Numerical Simulation Simulation Model Model Coupled Coupled with with Rock Rock Geomechanics Geomechanics After History After History Matching Matching 24.6–30.7 MPa 24.6–30.7 MPa 0.5–2.5 MPa 0.5–2.5 GPa MPa11 of 21 16.1–29.2 0.25–0.34 16.1–29.2 GPa 0.25–0.34 Figure Figure 13 13 shows shows the the heterogeneous heterogeneous numerical numerical simulation simulation model model properties. properties. Using Using the geomechanical parameters and the reservoir heterogeneous properties, the geomechanical parameters and the reservoir heterogeneous properties,aa 6X 6X numerical numerical simulator was chosen chosen to to build buildaatypical typicalheterogeneous heterogeneouswell well model investigate simulator was model to to investigate thethe ininfluencing factors,including includingboth bothreservoir reservoirproperties propertiesand andgeomechanical geomechanicalfactors. factors. fluencing factors, Figure 13. 13. Heterogeneous Heterogeneous numerical numerical simulation simulation model model properties. properties. (a) (a) Porosity Porosity model; model; (b) (b) minimum minimum Figure horizontal principal principal stress stress model. model. horizontal According According to to the the calibrated calibrated geological geological and and in situ stress parameters of the study area, aa coupling coupling numerical numerical simulation simulation mechanism mechanismmodel model of of aa reservoir reservoir and and fracture fracture system system was was established, in which the influence of formation longitudinal heterogeneity and well and established, in which the influence of formation longitudinal heterogeneity and well and fracturing parameter changes on fracturing effect is simulated, and the sensitive factors of fracturing effect in the study area are determined. The values of key properties in the sensitivity model are listed in Table 2. Based on the calibrated model presented in Section 3, the influence of reservoir Energies 2023, 16, 312 11 of 20 fracturing parameter changes on fracturing effect is simulated, and the sensitive factors of fracturing effect in the study area are determined. The values of key properties in the sensitivity model are listed in Table 2. Table 2. Spatial distribution of fractures under heterogeneous conditions. Category Geological and geomechanical parameters Fracturing parameters Item Level 1 Level 2 Level 3 Level 4 Level 5 Payzone thickness (m) Permeability (mD) Interlayer thickness (m) Stress difference between payzone and interlayer (MPa) Poisson’s ratio Young’s modulus (GPa) 2 0.2 6 3 0.5 8 4 0.8 10 5 1.2 12 6 1.5 14 0.5 1 1.5 2 2.5 0.2 15 0.25 20 0.3 25 0.35 30 / / 4 6 8 10 12 250 300 350 400 450 16 5 18 10 20 15 22 20 24 25 Injection rate (m3 /min) Liquid intensity (m3 /m) Sand intensity (m3 /m) Fracturing spacing (m) Based on the calibrated model presented in Section 3, the influence of reservoir payzone thickness, formation permeability, interlayer thickness, stress difference between payzone and interlayer, Young’s modulus and Poisson’s ratio on post-fracturing production was studied by mechanism numerical simulation, and the production performance curve was analyzed to determine the effect of sensitive factors. 4.2. Influence of Geological and Geomechanical Parameters on Volumetric Fracturing (1) Payzone thickness Figure 14a shows the permeability distribution after fracturing, and Figure 14b shows 12 of 21 the permeability distribution after 6 years of production. Based on a comparison of simulation results, vertical well volumetric fracturing and production were simulated under payzone thicknesses of 2 m, 3 m, 4 m and 5 m. The simulation results show that with an an increase reservoir payzone thickness, thelength/width length/widthratio ratio of of the the fracture fracture network increase in in reservoir payzone thickness, the network increases. increases. The The fracture fracture length length ranges ranges from from 145 145 m m to to 170 170 m, m, and and the the maximum maximum fracturefractureeffected width is 50 m. When the payzone thickness is 55m, m,there there is is aa significant significant change change in in fracture dimensions dimensions compared compared with withother otherfractures. fractures.AAcomparison comparisonofoffracture fracturepermeability permeability shows as production continues, fracture gradually closes. shows thatthat as production continues, the the fracture gradually closes. Energies 2023, 16, x FOR PEER REVIEW Figure 14. 14. Planar Planar fracture fracture permeability Figure permeability distribution distributionwith withdifferent differentpayzone payzonethickness thicknessvalues. values.(a)(a)After Affracturing; (b) (b) after 6 years of production. ter fracturing; after 6 years of production. Figure 15 15 shows shows that that the the oil oil saturation distribution is is basically basically consistent consistent with with the the Figure saturation distribution fracture network network configuration configuration at at the As production production continues, continues, the the fracture the end end of of fracturing. fracturing. As range of reservoir production expands relative to the fractured volume. After 6 years of range of reservoir production expands relative to the fractured volume. After 6 years of production, the plane length and width of the oil drainage area range from 310m to 370m and from 50m to 70m, respectively. Figure 14. Planar fracture permeability distribution with different payzone thickness values. (a) After fracturing; (b) after 6 years of production. Energies 2023, 16, 312 12 ofthe 20 Figure 15 shows that the oil saturation distribution is basically consistent with fracture network configuration at the end of fracturing. As production continues, the range of reservoir production expands relative to the fractured volume. After 6 years of production, the the plane plane length length and andwidth widthof ofthe theoil oildrainage drainagearea arearange rangefrom from310 310m to370 370m production, m to m and from 50 50m to 70m, respectively. m to 70 m, respectively. Figure 15. distribution with different payzone thickness values. (a) After fracFigure 15. Planar Planaroil oilsaturation saturation distribution with different payzone thickness values. (a) After turing; (b) after 6 years of production. fracturing; (b) after 6 years of production. indicted by bythe theperspectives perspectivesofofproduction productioneffect effect shown Figure cumulaAs indicted shown in in Figure 16,16, thethe cumulative tive production increases with payzone thickness, andaccumulative accumulativeoil oilproduction production varies production increases with payzone thickness, and greatly with different different reservoir reservoir thicknesses. thicknesses. The Thecumulative cumulativeoil oilproduction productionafter after3 3and and6 3 3 3 3 years with a reservoir thickness of 5 m is 324.71 m and 394.74 m higher, respectively, 6 years with a reservoir thickness of 5 m is 324.71 m and 394.74 m higher, respectively, than with reservoir thickness of 2 m of because the condition of the reservoir itself is the material than awith a reservoir thickness 2 m because the condition of the reservoir itself is the basis for basis the size and the productivity of a single Therefore, the material forof thetight sizeoil of reserves tight oil reserves and the productivity of awell. single well. ThereEnergies 2023, 16, x FOR PEER REVIEW 13 ofbe 21 development scale and scale reservoir characteristics of a “sweet should fore, the development and geological reservoir geological characteristics ofspot” a “sweet spot” carefully when designing should beconsidered carefully considered when fracturing designing schemes. fracturing schemes. Figure16. 16.Production Productionperformance performance curves of different payzone thickness values (6 years of proFigure curves of different payzone thickness values (6 years of production). duction). (2) Formation permeability (2) Formation permeability Based on the production effect comparison shown in Figure 17, the liquid production Based on the comparison shown in Figure the 0.5 liquid remains almost theproduction same wheneffect the formation permeability changes17, from mDproduction to 2.5 mD remains almost the same when the formation permeability changes from 0.5under mD to 2.5 because the flow capacity is mostly correlated with the fractured performance such mD because levels the flow is mostlyby correlated the fractured performance under permeability butcapacity is less impacted the initialwith formation permeability. such permeability levels but is less impacted by the initial formation permeability. In contrast, the oil production increases with a decrease in formation permeability, but the range of change is relatively small because the initial formation permeability has little influence on the fractured volume, which is mostly controlled by fracturing operations. Energies 2023, 16, 312 remains almost the same when the formation permeability changes from 0.5 mD to 2.5 mD because the flow capacity is mostly correlated with the fractured performance under such permeability levels but is less impacted by the initial formation permeability. In contrast, the oil production increases with a decrease in formation permeability, but the range of change is relatively small because the initial formation permeability has 13 of 20 little influence on the fractured volume, which is mostly controlled by fracturing operations. Figure 17. 17. Production (6(6 years of Figure Production performance performance curves curveswith withdifferent differentformation formationpermeability permeabilityvalues values years production). of production). In contrast, the oil production increases with a decrease in formation permeability, but 14 of 21 the range of change is relatively small because the initial formation permeability has little influence on the fractured volume, which is mostly controlled by fracturing operations. Energies 2023, 16, x FOR PEER REVIEW (3) Interlayer thickness (3) Interlayer thickness Figure production and accumulative oil production Figure18 18shows showsthat thataccumulative accumulativeliquid liquid production and accumulative oil producincrease with increased interlayer thickness, and accumulative oil production varies tion increase with increased interlayer thickness, and accumulative oil productiongreatly varies with different interlayer thicknesses. Cumulative oil production after 3 and 6 years an greatly with different interlayer thicknesses. Cumulative oil production after 3 andwith 6 years 3 and 371.70 3 higher,3respectively, than with an interlayer thickness of 14 m is 357.32 m m 3 with an interlayer thickness of 14 m is 357.32 m and 371.70 m higher, respectively, than interlayer thicknessthickness of 6 m because longitudinal fracture height is strongly by with an interlayer of 6 mthe because the longitudinal fracture height affected is strongly interlayer thickness. affected by interlayer thickness. Figure 18. Production curves with different interlayer thickness values (6 years Figure Productionperformance performance curves with different interlayer thickness values (6 of years production). of production). (4) (4) Stress difference of the payzone and interlayer interlayer Figure 19a shows the permeability distribution after fracturing, and Figure 19b shows the permeability distribution after 6 years of production. The simulation results show that with an increase in stress difference, the length and bandwidth of the target layer exhibit an increasing trend. The distribution range is 165–195 m, and the maximum Energies 2023, 16, 312 Figure 18. Production performance curves with different interlayer thickness values (6 years of production). 14 of 20 (4) Stress difference of the payzone and interlayer Figure 19ashows showsthe the permeability distribution fracturing, and Figure 19b Figure 19a permeability distribution afterafter fracturing, and Figure 19b shows shows the permeability distribution after 6 years of production. The simulation results the permeability distribution after 6 years of production. The simulation results show that showan that with an increase in stress the difference, thebandwidth length andof bandwidth of theexhibit target with increase in stress difference, length and the target layer layer exhibit an increasing trend. The distribution rangem, is and 165–195 m, and thebandwidth maximum an increasing trend. The distribution range is 165–195 the maximum bandwidth is 30 m, along expanding along theofdirection the seam length. As production is 30 m, expanding the direction the seamoflength. As production progresses,prothe gresses, fractures gradually andthe the smaller the stress the faster the fracturesthe gradually close, and theclose, smaller stress difference, thedifference, faster the closure, which closure, which is positively the degree of deficit. is positively correlated withcorrelated the degreewith of deficit. Energies 2023, 16, x FOR PEER REVIEW 15 of 21 Figure 19. Planar fracture fracture permeability permeabilitydistribution distributionwith withvarying varying stress difference. After fracturstress difference. (a)(a) After fracturing; ing; (b) after 6 years of production. (b) after 6 years of production. As liquid production andand accumulative oil producAs shown shownininFigure Figure20, 20,accumulative accumulative liquid production accumulative oil protion increase with the stress difference between the reservoir and interlayer. The cumulative duction increase with the stress difference between the reservoir and interlayer. The cuincremental oil production after 3 and after 6 years with6ayears stresswith difference 2.5 MPa isof 101.98 m3 mulative incremental oil production 3 and a stressofdifference 2.5 MPa 3 and 137.14 higher the casethan with a stress difference 0.5 MPa. of Therefore, 3 and is 101.98 mm 137.14than m3 higher the case with a stressofdifference 0.5 MPa. higher Therereservoir stress difference is more conducive to achieving effective reservoir fracturing and fore, higher reservoir stress difference is more conducive to achieving effective reservoir better production effect. fracturing and better production effect. Figure Figure 20. 20. Production Production performance performance curves curves with withvarying varyingstress stressdifference difference(6 (6years yearsof ofproduction). production). (5) Young’s modulus (5) Young’s modulus Figure 21 different Young’s modulus Figure 21shows showsthe theoil oilproduction productionperformance performancecurves curvesfor for different Young’s moduvalues. From the perspective of production effect, accumulative liquid production lus values. From the perspective of production effect, accumulative liquid productionand and accumulative oil production are basically the same, so Young’s modulus is not considered a sensitive factor. Figure 20. Production performance curves with varying stress difference (6 years of production). Energies 2023, 16, 312 (5) Young’s modulus 15 of 20 Figure 21 shows the oil production performance curves for different Young’s modulus values. From the perspective of production effect, accumulative liquid production and accumulativeoil oilproduction productionare arebasically basicallythe thesame, same, so so Young’s Young’smodulus modulusisisnot not considered considered accumulative sensitive factor. factor. aa sensitive Energies 2023, 16, x FOR PEER REVIEW 16 of 21 Figure Figure21. 21. Production Productionperformance performancecurves curvesfor fordifferent differentYoung’s Young’smodulus modulusvalues. values. (6) (6) Poisson’s Poisson’s ratio ratio Figure 22 indicates Figure 22 indicates that that the the liquid liquid production production isis the the same same when whenthe thePoisson’s Poisson’s ratio ratio varies from 0.2 to 0.35, but the oil production increases with a decrease in Poisson’s varies from 0.2 to 0.35, but the oil production increases with a decrease in Poisson’s ratio, ratio, whereas whereasthe theoverall overalldifference differenceisisnot notsignificant. significant. Figure22. 22.Production Productionperformance performancecurves curvesfor fordifferent differentPoisson’s Poisson’sratio ratiovalues. values. Figure 4.3. 4.3. Influence Influence of of Fracturing FracturingParameters Parameterson onVolumetric VolumetricFracturing Fracturing The mechanism numerical model is used to study thethe influence of injection rate, liquid The mechanism numerical model is used to study influence of injection rate, liqintensity, sand intensity and fracturing spacing of horizontal wells on the oil production uid intensity, sand intensity and fracturing spacing of horizontal wells on the oil produceffect after after fracturing, analyze the productivity change curvecurve and determine the sensitive tion effect fracturing, analyze the productivity change and determine the senfactors. Figures 23 and 24 show that the variation of each fracturing parameter is sensitive sitive factors. Figures 23 and 24 show that the variation of each fracturing parameter is to the productivity. Oil production increases with increases construction displacement, sensitive to the productivity. Oil production increases within increases in construction dis- placement, liquid volume and sand volume and increases with decreased fracture spacing of horizontal wells. The oil production changes with changes in the liquid injection rate and liquid volume, whereas with changes in sand volume and fracture spacing of horizontal wells, the oil production varies significantly. Figure 22. Production performance curves for different Poisson’s ratio values. 4.3. Influence of Fracturing Parameters on Volumetric Fracturing Energies 2023, 16, 312 The mechanism numerical model is used to study the influence of injection rate, liquid intensity, sand intensity and fracturing spacing of horizontal wells on the oil producof 20 tion effect after fracturing, analyze the productivity change curve and determine the 16 sensitive factors. Figures 23 and 24 show that the variation of each fracturing parameter is sensitive to the productivity. Oil production increases with increases in construction disliquid volume and sand volume and increases decreased spacing of horizontal placement, liquid volume and sand volume andwith increases withfracture decreased fracture spacing The oil production changes with changes the liquid injection and liquid ofwells. horizontal wells. The oil production changes withinchanges in the liquidrate injection rate volume, changes sand volume fracture horizontal wells, and liquidwhereas volume, with whereas withinchanges in sandand volume andspacing fractureofspacing of horithe oilwells, production varies significantly. zontal the oil production varies significantly. Energies 2023, x FOR PEER REVIEW Energies 2023, 16,16, x FOR PEER REVIEW 1717of of2121 Figure Injection rate and liquid injection intensity sensitivity. Figure 23.23. Injection rate and liquid injection intensity sensitivity. Figure 24. Sand volume intensity and fracturing spacing sensitivity. Figure Sand volume intensity and fracturing spacing sensitivity. Figure 24.24. Sand volume intensity and fracturing spacing sensitivity. Pilot Well Design and Oil Production Performance Design and Oil Production Performance 5.5.Pilot Well Design and Oil Production Performance The pilot well was placed in the WTG formation group in Xinjiang Oilfield, which is The pilot well was placed in the WTG formation group in Xinjiang Oilfield, which is yet to be exploited due to the ultra-low permeability in in this tight oiloil reservoir. TheThe evolution yet to exploited due the ultra-low permeability this tight reservoir. evoluyet to bebe exploited due toto the ultra-low permeability in this tight oil reservoir. The evoluof hydraulic volumetric fracturing technology makes it possible to realize commercialized tionofofhydraulic hydraulicvolumetric volumetricfracturing fracturingtechnology technologymakes makesit itpossible possibletotorealize realizecommercommertion development throughthrough careful fracturing design. design. The success of the pilot well iswell of great cialized development careful fracturing The success of the pilot cialized development through careful fracturing design. The success of the pilot well is is ofof significance to the development of this area, so the methodology proposed in this study great significance to the development of this area, so the methodology proposed in this great significance to the development of this area, so the methodology proposed in this was used toused guide the fracturing design.design. study was to guide the fracturing study was used to guide the fracturing design. Using the workflow proposed this study, the typical pilot well this area was Usingthe theworkflow workflowproposed proposedinin inthis thisstudy, study,the thetypical typicalpilot pilotwell wellinin inthis thisarea areawas was Using chosen, and geomechanical modeling was performed based on the petrophysical properties. chosen, and geomechanical modeling was performed based the petrophysical properchosen, and geomechanical modeling was performed based onon the petrophysical properFigure 25 displays the 3D porosity and minimum principal stressstress modeling results. The ties. Figure 25 displays the 3D porosity and minimum principal modeling results. ties. Figure 25 displays the 3D porosity and minimum principal stress modeling results. grid dimensions are 20 m × 10 m × 0.2–1 m, and the total grid number is 328,891. The grid dimension are × 10 × 0.2–1 and the total grid number 328,891. The grid dimension are 2020 mm × 10 mm × 0.2–1 m,m, and the total grid number is is 328,891. Figure Three-dimensional porosity and minimum principal stress modeling results. Porosity; Porosity; Figure 25.25. Three-dimensional porosity and minimum principal stress modeling results. (a)(a) Porosity; (b) minimum principal stress. (b)minimum minimumprincipal principalstress. stress. (b) Basedononthe thegeomechanical geomechanicalmodel, model,the thehydraulic hydraulicfracturing fracturingparameters parameterswere werededeBased signed,and andthe theproduction productionperformance performancewas wasforecasted; forecasted;the thehydraulic hydraulicfracturing fracturingdesign design signed, results for each zone are listed in Table 3. results for each zone are listed in Table 3. Figure2626shows showsthe thefracture fractureevolution evolutionafter afterfracturing, fracturing,and andFigure Figure2727displays displaysthe the Figure horizontal permeability changes after fracturing and after two years of production. Based horizontal permeability changes after fracturing and after two years of production. Based Energies 2023, 16, 312 17 of 20 Based on the geomechanical model, the hydraulic fracturing parameters were designed, and the production performance was forecasted; the hydraulic fracturing design results for each zone are listed in Table 3. Table 3. Results of the pilot well after hydraulic fracturing design optimization. Zone No. Slickwater m3 Water m3 1# 405 135 2# 1480 490 3# 2350 750 4# 1740 560 5# 795 265 6# 1125 375 Energies2023, 2023,16, 16,xxFOR FOR PEER REVIEW Energies 7# 855 PEER REVIEW 285 3# 3# 4# 4# 5# 5# 6# 6# 7# 7# 2350 2350 1740 1740 795 795 1125 1125 855 855 Preflush m3 Sand-Carrying Fluid m3 Displacement Fluid m3 Total Liquid m3 100 Mesh m3 40–70 Mesh m3 20–40 Mesh m3 Total Sand 16 50 100 60 28 36 28 75.7 265.6 563.1 330.9 152.2 199.6 152 7.3 7.8 7.7 7.7 7.5 7.4 7.3 639 2293.4 3770.8 2698.6 1247.7 1743 1327.3 3 9 14 11 5 7 5 17 69 113 84 35 50 38 3 5 8 5 5 5 5 23 83 135 100 45 62 18 of of 21 18 48 21 Figure 26 shows the fracture evolution after fracturing, and Figure 27 displays the 750 100 563.1 7.7 3770.8 14 113 135 750 100 563.1 7.7 3770.8 14 113 88 135 horizontal permeability changes after fracturing and after two years of production. Based 560 on the simulation 60 330.9 the main 7.7fracture2698.6 2698.6 11 84 100 560 60 330.9 7.7 11 55 of the branch 100 results, length is 223–341 m,84 the length 265 fractures28 28 152.2 7.5 volume 1247.7 45 265 7.5 1247.7 55 106 m3 35 55 permeability 45 is 146–234152.2 m and the fractured is 1.85 × .35 The overall 375 after fracturing 36 199.6 7.4 1743 50 62two is 535 mD, and it7.4 gradually1743 declines to77260 mD50 after production for 375 36 199.6 55 62 285years, which 28 is still much 152 higher than 7.3 that before 1327.3fracturing. 38 48 285 28 152 7.3 1327.3 55 38 55 48 Figure26. 26.Fracture Fractureevolution evolutionafter afterfracturing. fracturing. Figure Fracture evolution Figure 27. Horizontalpermeability permeabilitychanges changesover overtime. time.(a) After fracturing; after two years Figure 27. 27. Horizontal Horizontal permeability changes over time. (a)(a) After fracturing; (b)(b) after two years of Figure After fracturing; (b) after two years of production. of production. production. As inFigure Figure28, 28, the production performance of optimized the optimized fracturing Asshown shownin in Figure 28, the production performance ofthe the optimized fracturing pilot As the production performance of fracturing pilot pilot well was predicted and compared with that of the adjacent well, which was treated well was predicted and compared with that of the adjacent well, which was treated by well was predicted and compared with that of the adjacent well, which was treated by by traditional commingle hydraulic fracturing. It is obvious that the highest oil rate of traditional commingle commingle hydraulic hydraulic fracturing. fracturing. ItIt isis obvious obvious that that the the highest highest oil oil rate rate of of the the traditional the pilot well is 12 tons/day, which is enhanced by 7 tons/day relative to traditional pilotwell wellisis12 12tons/day, tons/day,which whichisisenhanced enhancedby by77tons/day tons/dayrelative relativeto totraditional traditionalfracturing. fracturing. pilot fracturing. Furthermore, the oil rate declination rate also quiteAfter different. After year Furthermore, theoil oilrate ratedeclination declination rateisisalso alsoquite quiteisdifferent. different. After oneyear year ofone producFurthermore, the rate one of produc- tion,the theoil oilrate rateremains remainsat at6.3 6.3tons/day, tons/day,whereas whereasthat thatof oftraditional traditionalfracturing fracturingreaches reachesthe the tion, economic limit of production (2.5 tons/day). It is predicted that the total incremental oil of economic limit of production (2.5 tons/day). It is predicted that the total incremental oil of thefirst firstyear yearcould couldreach reach2030 2030tons. tons. the Energies 2023, 16, 312 18 of 20 19 of 21fracturing of production, the oil rate remains at 6.3 tons/day, whereas that of traditional reaches the economic limit of production (2.5 tons/day). It is predicted that the total incremental oil of the first year could reach 2030 tons. 6, x FOR PEER REVIEW Figurecomparison 28. Productivity of theafter pilot tradition well after tradition fracturing optimized fracturing. Figure 28. Productivity of comparison the pilot well fracturing and and afterafter optimized fracturing. 6. Conclusions A workflow of a 3D fine geomechanical model was proposed, including a structure model, petrophysical model and geomechanical model. The geomechanical model A workflow of a parameters 3D fine geomechanical modelwere wascomprehensively proposed, including a structure of a typical reservoir corrected through production history matching. model, petrophysical model and geomechanical model. The geomechanical model (2) a typical The sensitive factors affecting fracturing production this area were evaluated parameters of reservoir were comprehensively corrected in through producnumerically. The influence of formation parameters and operational parameters on tion history matching. volume fracturing was studied with oil production as the main index. The results The sensitive factors affecting fracturing production in this area were evaluated nushow that for formation parameters, the payzone thickness of the reservoir is the main merically. The influence of formation parameters and operational parameters on volinfluencing factor; the interlayer thickness and stress difference between the reservoir ume fracturing was with as the main index. show andstudied interlayer are oil the production secondary influencing factors; andThe the results formation permeability, that for formation parameters, the payzone thickness of the reservoir is the main Young’s modulus and Poisson’s ratio are the weak influencing factors. influencing factor; thetypical interlayer thickness anddesigned, stress difference thewere reservoir (3) A pilot test well was fracturingbetween parameters optimized and and after optimized was predicted and compared. and interlayer arethe theproduction secondarybefore influencing factors; andfracturing the formation permeability, The results show that optimized fracturing can increase the oil production rate by Young’s modulus and Poisson’s ratio are the weak influencing factors. 7 tons/day relative to traditional fracturing. The oil production rate is 4 tons/day A typical pilot test well was designed, fracturing parameters were optimized and the higher than that of conventional fracturing after 1 year of production, indicating production before and after optimized fracturing was predicted and compared. The encouraging incremental performance. 6. Conclusions (1) (2) (3) (1) results show that optimized fracturing can increase the oil production rate by 7 tons/day relative to traditional fracturing. The oil production rate is 4 tons/day higher Author Contributions: D.D. designed and conducted the experiments and wrote the main manuscript than that of text; conventional fracturing after 1 year of production, indicating encouragY.W. conducted numerical simulation; X.X. revised the main manuscript text; W.L. designed ing incremental performance. the experiment; J.Z. and P.L. prepared all of the figures. All authors have read and agreed to the published version of the manuscript. Author Contributions: D.D. designed and conducted the experiments and wrote the main manuFunding: This research was funded by the China National Key Project (2016ZX05031) and the Science script text; Y.W. conducted numerical simulation; X.X. revised main manuscript text; W.L. deand Technology Project of CNPC (2021DJ3208 andthe 2021DJ1403). signed the experiment; J.Z. and P.L. prepared all of the figures. All authors have read and agreed to Institutional Review Board Statement: Not applicable. the published version of the manuscript. Informed Consent Statement: Not applicable. Funding: This research was funded by the China National Key Project (2016ZX05031) and the Science and Technology Project of CNPC (2021DJ3208 and 2021DJ1403). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Energies 2023, 16, 312 19 of 20 Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. 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