1
1
UNDISCOVERED
2
PROSPECT, NORTHEAST THAILAND
3
HYDROCARBON
RESOURCES
OF
CHONNABOT
Sakchai Glumglomjit* and Akkhapun Wannakomol
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Running head: Undiscovered Hydrocarbon Resources of Chonnabot prospect,
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Northeast Thailand
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Abstract
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The objective of this research is to assess the hydrocarbon resources in the
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Chonnabot prospect, an important Permian carbonate hydrocarbon source and
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reservoir rock of northeast Thailand. The study area covers the area of
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Chonnabot, Waeng Yai, and Waeng Noi districts, Khon Kaen province in the
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southwestern part of northeastern region of Thailand. The assessment of
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undiscovered hydrocarbon resources in the Chonnabot prospect was performed
13
by using play analysis, probability theory approach, and FASPU computer
14
program. The resulting mean estimates are 15.02 MMbbl of undiscovered oil and
15
657.53 Bcf of undiscovered non-associated gas.
16
17
Keywords: Undiscovered hydrocarbon resources, petroleum potential
18
assessment, play analysis, Chonnabot prospect, northeast Thailand
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*
School of Geotechnology, Institute of Engineering, Suranaree University of Technology,
111 University Avenue, Muang District, Nakhon Ratchasima 30000, Thailand.
Email: sakchaig@hotmail.com
*
Corresponding Author
2
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Introduction
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Exploration and production of petroleum in the northeast of Thailand has been going
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on for more than four decades, and today only two natural gas fields named Nam
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Phong and Sinphuhorm are on production. The petroleum provinces in the northeast
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still have a high potential for exploration and development (Atop, 2006; GMT 1999).
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The reservoir rocks in this vast region are mainly Permian carbonates which contain
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anticlines resulting from transversing fault lines, creating fractures, and adding
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porosity to the carbonates. More than 30 wells drilled were confirmed petroleum in
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the northeast and Permian carbonate reservoir rocks have potential.
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The Chonnabot prospect is chosen for this assessment since there is some gas
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found in Connabot-1 well (Esso, 1982) in Permian carbonate rock like in the Nam
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Phong and the Sinphuhorm gas fields. This project covers the area of Chonnabot,
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Waeng Yai, and Waeng Noi districts, Khon Kaen province in the southwestern part of
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northeastern region of Thailand between latitudes 15o45´ to 16o15´ North and
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longitudes 102o15´ to 102o45´ East (Figure 1).
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Play analysis, probability theory approach, and FASPU computer program are
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used for estimating undiscovered hydrocarbon at a play scale within this prospect
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(CCOP, 1990; Charpentier et al., 1994).
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Materials and Methods
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Materials
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Required data for the undiscovered hydrocarbon assessment consist of two
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major parameters, which are petroleum geology parameters and petroleum
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engineering parameters. Petroleum geology parameters can be divided into three
3
46
parts: play attributes, prospect attributes, and hydrocarbon volume attributes. The
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first, play attributes consist of the probability of the existence of hydrocarbon source,
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timing, migration, and potential reservoir facies. The second, prospect attributes
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consist of probability of favorability for trapping mechanism, effective porosity, and
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hydrocarbon accumulation. The last, hydrocarbon volume attributes consist of area of
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closure, reservoir thickness/vertical closure, effective porosity, trap fill, reservoir
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depth, hydrocarbon saturation, and number of drillable prospects.
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Petroleum engineering attributes consist of some essential parameters for the
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quantity of hydrocarbon calculated by volume metric equation. These parameters are
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original reservoir pressure (psi), reservoir temperature (oR), gas-oil ratio (Mcf/bbl), oil
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formation volume factor, gas compressibility factor, oil floor depth, oil recovery
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factor, and gas recovery factor.
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All of required data for this assessment were compiled, reviewed, summarized,
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and documented from relevant literatures. These data were provided by the
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Department of Mineral Fuel (DMF), Ministry of Energy.
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Methods
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The geostochastic system for undiscovered hydrocarbon resources estimation
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in this study was performed by using Fast Appraisal System for Petroleum Universal
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(FASPU) computer program. The FASPU program is a prototype package of
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programs designed to assess the resource potential of undiscovered oil and gas
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resources using a play analysis method. A resource appraisal system is designed for
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play analysis using a reservoir engineering geologic model and an analytic
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probabilistic methodology. The geological model is a particular type of probability
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model using reservoir engineering equations. The probabilistic methodology is an
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analytic method derived from probability theory. A detailed discussion of the FASPU
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program is given by Crovelli and Balay (1994 and 1986).
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Play Analysis Approach
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Play analysis is a quantitative approach for estimating undiscovered
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hydrocarbon resource at a play scale. A play is a set of pools or prospects which are
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conceived as having similar geologic characteristics and sharing common geologic
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elements. Most of the input parameters used in the model are expressed in a
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probability form that is either as a probability of occurrence or as a probability
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distribution. This allows the uncertainty about the input parameters to be expressed
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quantitatively. Likewise, the resulting resource estimates are expressed as probability
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distributions in order to show the uncertainty of the estimates.
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Play analysis approach can be divided into two groups of parameter. In the
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first group, estimates are made of the favorability for resource in the play as a whole,
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as well as in a random prospect in the play. In the second group, estimates are made of
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the number of prospect and the size of the possible accumulation.
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In the first group of parameter, the probability of favorability for resource in
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the play as a whole (play attribute or marginal play probability) is estimated by
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judging the probabilities of existence of the subsidiary attributes of hydrocarbon
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source, timing, migration, and potential reservoir facies. The play can contain
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resources only if all four of these attributes exist. The play risk, the probability that
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the play contains no resource, is 1.0 minus the marginal play probability. Next, the
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prospect attribute or conditional deposit probability, the probability of occurrence of
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an accumulation in a random prospect (conditioned on the play attributes being
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favorable), is estimated by judging the probabilities of existence of the subsidiary
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attributes of trapping mechanism, effective porosity, and hydrocarbon accumulation.
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The prospect risk, the probability that a prospect contains no resource (again
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conditioned on the play attributes being favorable), is 1.0 minus the conditional
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deposit probability.
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In the second group of parameter, estimates are made of the number of
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prospect and also of various parameters which deal with the size of accumulations.
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Many of these variables are input as seven fractiles describing a probability
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distribution. Thus, the uncertainty about number of prospects is expressed by the
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seven fractiles for number of drillable prospects. The accumulation volumes are
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analytically calculated from the variables: area of closure (acre), reservoir
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thickness/vertical closure (feet), effective porosity (percent), trap fill (percent),
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reservoir depth (feet), and hydrocarbon saturation (percent). Also used in this
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calculation are such engineering parameters as original reservoir pressure (psi),
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reservoir temperature (oR), gas-oil ratio (Mcf/bbl), oil formation volume factor, gas
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compressibility factor, oil floor depth (feet), oil recovery factor (percent), and gas
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recovery factor (percent).
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The probabilities determined in the first group are used with the distributions
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of prospect number and accumulation size to estimate resource amounts. These
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resource appraisal estimates are calculated using an analytic probabilistic
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methodology. The details of how the calculations are performed can be found in
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Crovelli and Balay (1994 and 1986).
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Results and Discussions
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The results of the study of geological, geophysical, geochemical, and
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petroleum reservoir engineering can define the attribute and the probability for each
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attribute.
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The play attribute determination and analysis are mainly involved with the
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presence or absence of regional characteristics. The attribute determines whether
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conditions underlying the play are favorable for occurrence and accumulation of the
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hydrocarbon in the play level. The probability for each attribute is also shown in
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Table 1.
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Play type of the Chonnabot prospect is defined as carbonate reservoir rock of
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Permian age (Figure 2). According to a previous work, the possible source rock is the
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Pha Nok Khao Formation of the Saraburi Group which has a fair to excellent organic
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richness, and the thermal maturity of source rocks indicated a late to overmature stage
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(Thongboonruang, 2008). In this assessment, the probability of the existence of
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hydrocarbon source is given to 1.00 and the probability of favorable timing for
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migration of hydrocarbon from source to reservoir is given to 1.00. The probability of
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the existence of potential migration path is given to 0.90. This is due to the fractured
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carbonate reservoir. The probability of existence of potential reservoir facies is given
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to 0.90. This is due to the influence of numerous tectonically induced fractures and
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microfractures were partly filled with calcite. It is common and accounted for the
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majority of the porosity and permeability. The dolomitization of limestone also
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provides the secondary porosity which enhances reservoir quality (Chinoroje and
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Cole, 1995). Therefore, the marginal play probability for this prospect, the product of
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the probability of hydrocarbon source, timing, migration and the potential reservoir
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facies, is equal to 0.81.
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The prospect attribute determination and analysis within a play are mainly
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involved with the presence or absence of local characteristics. The attribute is
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generally favorable in a randomly selected prospect within the play area. The
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probability for each attribute is also shown in Table 1.
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The probability of existence of trapping mechanism is given to 1.0 because the
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seismic profile shown angular unconformity between the Permian carbonate rock and
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the Triassic Huai Hin Lat Group (Atop, 2006; GMT 1999). The probability of
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effective porosity is given to 0.80 because data from drilled-well in the study area
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(Esso, 1982) indicated that this carbonate reservoir rock has high average porosity.
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The probability of hydrocarbon accumulation is given to 1.00 since both reservoir and
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source rocks are the Permian carbonate (Figure 2). So, the chance of petroleum
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migration from source rock to its nearby trap rock is high. Therefore, the conditional
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deposit probability for the prospect level, the product of the probability of trapping
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mechanism, effective porosity and hydrocarbon accumulation, is equal to 0.80.
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The probability distribution for hydrocarbon volume attributes can be
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calculated from the plot of data and the observation of a complementary cumulative
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distribution function. Then, the favorable value at the fractile of 100th, 95th, 75th, 50th,
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25th, 5th, and 0th of each attribute, including area of closure, reservoir
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thickness/vertical closure, effective porosity, trap fill, reservoir depth, and
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hydrocarbon saturation, are estimated. In the play analysis approaching, the seven
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fractiles are estimated for all six of the hydrocarbon volume attributes. The probability
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for each attribute is also shown in Table 1.
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The reservoir engineering parameters consist of original reservoir pressure
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(psi), reservoir temperature (oR), gas-oil ratio (Mcf/bbl), oil formation volume factor,
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gas compressibility factor, oil floor depth (feet), and oil and gas recovery factor
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(percent). For the Chonnabot prospect, the study methodology also used the
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probability theory as in the hydrocarbon approaching. As a result, the relationship
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between pressure and depth, temperature and depth, and other essential reservoir
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engineering parameters are summarized and shown in the lower part of Table 1.
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The estimates of the total undiscovered recoverable quantities of hydrocarbon
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in the Chonnabot prospect are presented in the form of complementary cumulative
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probability as shown in Table 2. These distributions summarize the range of estimates
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generated by the FASPU program as a single probability curve in a "greater than"
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format. The assessments are reported at the mean and the level of confidence to 5
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levels as follows;
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- Very high confidence at the fractile of 95 (95th)
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- High confidence at the fractile of 75 (75th)
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- Medium confidence (most likely) at the fractile of 50 (50th)
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- Low confidence at the fractile of 25 (25th)
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- Very low confidence at the fractile of 5 (5th)
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As a result, oil in place resource within the Permian play of the Chonnabot
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prospect is estimated to 41.184 MMbbl from 1 oil accumulation at only 5 percent
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chance of discovery.
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Based on geochemical study of the Permian carbonate source rock in the study
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area, it indicates that the possible natural gas generated in the Chonnabot prospect
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could be only non-associated gas. The accumulation size of these non-associated gas
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resources can be summarized as follows; 1) at 95 percent chance of discovery, the
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accumulation size is 122.43 billion cubic feet (from 2 accumulations), 2) at 75 percent
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chance of discovery, the accumulation size is 270.90 billion cubic feet (from 4
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accumulations), 3) at 50 percent chance of discovery, the accumulation size is 470.44
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billion cubic feet (from 4 accumulations), 4) at 25 percent chance of discovery, the
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accumulation size is 816.99 billion cubic feet (from 5 accumulations), and 5) at 5
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percent chance of discovery, the accumulation size is 1,807.66 billion cubic feet (from
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6 accumulations), respectively.
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Conclusions and Recommendations
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Results from the estimation of the total undiscovered oil and gas resources in
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the Permian carbonate of the Chonnabot prospect from this study can be summarized
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as follows;
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1) The quantities of oil resource is 41.18 MMbbl but the chance of discovery
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is only 5 percent (fractile 5th). Therefore, there is a low or no chance to find an oil
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field within this prospect.
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2) The quantities of gas resources vary from 122.43 Bcf at very high
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confidence (fractile 95th), 270.90 Bcf at high confidence (fractile 75th), 470.44 Bcf at
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medium confidence (fractile 50th), 816.99 Bcf at low confidence (fractile 25th), and
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1,807.66 Bcf at very low confidence (fractile 5th), respectively.
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However, the reliability of petroleum prospect assessment depends mainly on
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the accuracy of the input parameters and the proposed geological model. The results
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of the assessment may be different if the new information on this play type is
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available. The undiscovered petroleum resources assessed by FASPU program can be
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used for decision making in the investment of petroleum exploration and production
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in the nearby prospect of northeastern Thailand. It is also useful in the prediction of
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the future petroleum business in this region.
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Acknowledgement
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The research work presented in this paper was supported by Suranaree University of
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Technology. The permission of the Department of Mineral Fuel, Ministry of Energy,
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to use required data for undiscovered hydrocarbon assessment is also greatly
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appreciated. The authors would like to thank Assoc. Prof. Kriangkrai Trisarn for his
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valuable suggestions.
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224
References
225
Atop Technology Co, Ltd. (2006). Unpublished data. Petroleum Assessment in
226
Northeastern Thailand. Department of Mineral Fuels, Ministry of Energy,
227
Thailand.
228
229
CCOP Technical Secretariat (1990). CCOP/WGRA Play Modelling Exercise 19891990. Bangkok, Thailand, 126p.
230
Chantong, W. (2007). Carbonate reservoir in the Khorat Plateau (in thai). Proceedings
231
of DMF Technical Forum 2007; May 18, 2007; Department of Mineral Fuels,
232
Bangkok, Thailand, p. 55-76.
233
Charpentier, R., Volgyi, L., Dolton, G., Mast, R., and Palyi, A. (1994). Undiscovered
234
recoverable oil and gas resources. In: Basin Analysis in Petroleum
235
Exploration: A case study from the Bekes basin, Hungary. Teleki, P.G.,
11
236
Mattick, R.E., and Kokai, J. (eds.). Kluwer Academic Publishers, Boston, p.
237
305-319.
238
Chinoroje, O. and Cole, M.R. (1995). Permian carbonates in the Dao Ruang#1
239
exploration well - Implications for petroleum potential, Northeast Thailand.
240
Proceedings of the International Conference on Geology, Geotechnology and
241
Mineral Resources of Indochina; November 22-25, 1995; Khon Kaen
242
University, Khon Kaen, Thailand, p. 563-576.
243
Crovelli, R.A. and Balay, R.H. (1994). Geologic model, probabilistic methodology
244
and computer programs for petroleum resource assessment. In: Basin Analysis
245
in Petroleum Exploration: A case study from the Bekes basin, Hungary.
246
Teleki, P.G., Mattick, R.E., and Kokai, J. (eds.). Kluwer Academic Publishers,
247
Boston, p. 295-304.
248
Crovelli, R.A. and Balay, R.H. (1986). FASP, an analytic resource appraisal program
249
for petroleum play analysis. Computers & Geosciences, 12(4): 423-475
250
Esso Exploration and Production Khorat Inc. (1982). Unpublished data. Geological
251
Completion Report: Chonnabot NO. 1.
252
GMT Cooporation Ltd. and Suranaree University of Technology (1999). Unpublished
253
data. Petroleum Potential Assessment of Northeastern Thailand. Mineral Fuels
254
Division, Department of Mineral Resources, Ministry of Industry, Thailand.
255
Thongboonruang, C. (2008). Petroleum source rock potential of NE Thailand.
256
Proceedings of the 2nd Petroleum Forum: Blooming Era of Northeastern
257
Thailand; September 15-16, 2008; Department of Mineral Fuels, Bangkok,
258
Thailand, p. 33-50.
259
12
102o
103o
104o
105o
Petroleum prospect numbers:
1. Nam Phong 6. Mukdahan
11. That Phu Wong
16. Yang Talat
2. Phu Horm
7. Phu Khieo
12. Phu Phra
17. Lam Pao
3. Chonnabot
8. Kuchinarai
13. Sakon
4. Dong Mun
9. Kaset Sombun
14. Kham Palai
5. Si That
10. Phu Kao
15. Non Sung
Successful Potential Prospects
Unproved Potential Prospects
Untested Potential Prospects
18o
18o
23
LOEI
19
27
5
30
16
17o
26
57
14
8
6
33
32
34
35
16
21
18
25
31
1
KHON KAEN
9
24
17
4
7
13
29
10
20
12
11
28
2
17o
NAKHON PHANOM
58
UDON THANI
37
15
o
3
43
51
48
44
22
16o
Chonnabot prospect (study area)
49
36
52
50
45
46
UBON RATCHATHANI
47
42
15o
53
54
38
15o
41
NAKHON RATCHASIMA
56
40
39
59
55
50 km
102o
103o
104o
105o
Figure 1. The petroleum prospects map of northeastern region of Thailand
(Chantong, 2007)
13
Figure 2. Lithostratigraphy and petroleum system of northeastern region of
Thailand (Chantong, 2007)
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Table 1. Input data for hydrocarbon resource assessment by the FASPU program
of the Chonnabot prospect, Carbonate Play
INPUT
Play Attributes
Prospect Attributes
Hydrocarbon Volume
Attribute
Probability of Favorable
Hydrocarbon Source
1.00
Timing
1.00
Migration
0.90
Potential Reservoir Facies
0.90
Marginal Play Probability
0.81
Trapping Mechanism
1.00
Effective Porosity (>3%)
0.80
Hydrocarbon Accumulation
1.00
Conditional Deposit Probability
0.80
Reservoir Lithology
Parameter
Hydrocarbon type
Sand
--
Carbonate
X
Gas
0.95
Oil
0.05
Probability of equal to or greater than
Attribute
100
95
75
50
25
5
0
Area of Closure (1,000 acres)
1.01
1.66
4.06
7.07
10.07
12.47
13.07
Reservoir Thickness (feet)
100
120
205
234
256
323
340
Effective Porosity (%)
3.00
3.14
3.71
4.61
7.00
13.10
18.00
30
35
40
45
50
70
80
11.40
11.65
12.65
13.90
15.15
16.15
16.40
HC Saturation (%)
60
64
72
82
86
89
90
No. of drillable prospects
5
5
5
6
7
8
9
Trap Fill (%)
Reservoir Depth (1,000 feet)
Original reservoir pressure (psi)
= (0.7166 x Depth) + 14.5038
Reservoir temperature (oR)
= (0.0267 x Depth) + 538.00 (from 0-2,300 feet)
= (0.0068 x Depth) + 579.00 (from 2,300-5,500 feet)
= (0.0115 x Depth) + 537.00 (below 5,500 feet)
Gas-oil ratio (Mcf/bbl)
= 0.0056146
Oil formation volume factor
= 1.00
Gas compressibility factor
= (0.00001 x Depth) + 1.02384
Oil floor depth
14,870 feet
Oil and gas recovery factor
Oil
Free gas
5.00
90.00
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Table 2. Results of hydrocarbon resource assessment by the FASPU program of
the Chonnabot prospect, Carbonate Play
Result
Mean
F95
F75
F50
F25
F05
0.161
0
0
0
0
1
15.015
2.815
6.212
10.768
18.664
41.184
1.958
0
0
0
0
13.877
4.479
3
4
4
5
7
657.53
122.43
Oil resource (MMbbl)
Number of accumulations
Accumulation size
Unconditional play potential
Non-associated gas resource (Bcf)
Number of accumulations
Accumulation size
Unconditional play potential
2,345.50
0
270.90
470.44
816.99
1,807.66
1,250.23
2,232.27
3,337.20
5,644.70