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SOFTWARE DEVELOPMENT FOR NATURAL GAS FIELD
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POTENTIAL ASSESSMENT AT CHONNABOT PROSPECT,
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NORTHEASTERN THAILAND
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Running head: Software Development for Natural Gas Field Potential
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Assessment at Chonnabot Prospect
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Kanjana Ruksutjaritkul* Kriangkrai Trisarn and Akkhapun Wannakomol
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Received: Jun 9, 2011; Revised: Jan 25, 2012; Accepted: Jan 25, 2012
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Abstract
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The objectives of this research are (1) to develop the computer program called
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PPA (Petroleum Potential Assessment) using Microsoft Visual Basic version 6.0
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for determining for natural gas field potential assessment, (2) to compare the
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commercial petroleum potential assessment software, including FASPU (Fast
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Appraisal System for Petroleum Universal Version) and GeoX (Geometry
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Experiments) with PPA (Petroleum Potential Assessment), and (3) to evaluate
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economic returns in terms of Internal Rate of Return (IRR) and Profit to
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Investment Ratio (PIR). The PPA software has been developed for natural gas
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potential assessment in the northeastern Thailand and design support of input
*
School of Geotechnology, Institute of Engineering, Suranaree University of Technology, 111
University Avenue, Muang District, Nakhon Ratchasima 30000, Thailand.
E-mail: Kanjana_kul@hotmail.com
*
Corresponding author
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parameter under various geological and engineering parameters including area
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of closure, porosity, thickness, gas saturation, gas formation volume factor, and
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recovery factor. PPA software uses the Monte Carlo simulation and probability
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of success theory, and is developed on Microsoft Visual Basic version 6.0. The
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software includes a main page and three modules: technical considerations, gas
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production forecast considerations and economic considerations. In terms of
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technical consideration, the estimated gas in place resources are 147.49 Bcf
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(billion cubic feet) at 90%, 405.71 Bcf at 50% and 926.74 Bcf at 10% of fractile,
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respectively. The estimated gas recoverable resources are 132.74 Bcf at 90%,
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365.14 Bcf at 50% and 834.07 Bcf at 10% of fractile, respectively. In terms of
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economic consideration, the Internal Rate of Return (IRR) and Profit to
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Investment Ratio (PIR) for natural gas field at Chonnabot prospect are 20.03%
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and 1.10, respectively.
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Keywords: Northeastern Thailand, software development, Chonnabot prospect
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Introduction
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Due to petroleum prices, the world has an economic problem. Energy is vital to both
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the economy and population since it is one of the main powers driving the growth of
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economics and the prosperity of people. The energy consumption has developed in
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various functions such as industry, agriculture, and transportation. Thailand spends a
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large amount of money importing oil because reserves of petroleum in Thailand are
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limited. Therefore, we should be searching for new petroleum fields and increasing
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recovery from the existing petroleum fields.
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As in the 2007 annual report, the Department of Mineral Fuels Strategic plan
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is to promote petroleum assessment through greater data security, update, and
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organize annual technical seminars to share views with concessionaries in
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northeastern Thailand, academics and the interested public to add further insight and
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ease exploration success. Exploration and production of petroleum in northeastern
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Thailand has been going on for more than four decades. Currently, only two natural
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gas fields, namely Nam Phong and Sinphuhorm, are on production. The petroleum
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provinces in northeastern Thailand have a high potential for exploration and
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development. The reservoir rocks in this vast region are Permian carbonates, which
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contain anticlines resulting from transversing fault lines, creating fractures, and
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adding porosity to the carbonates. More than 30 wells drilled have confirmed this fact,
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but to date only the Nam Phong and the Sinphuhorm gas fields have production
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(Department of Mineral Fuels, 2007). It is expected that more gas fields will be
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developed in the carbonate reservoir rock Chonnabot prospect.
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Methods
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For this study, we used three computer softwares consisting of FASPU (Fast
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Appraisal System for Petroleum Universal Version), GeoX (Geometry Experiments)
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and PPA (Petroleum Potential Assessment). The required data for the natural gas
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potential assessment consists of two parameters, which are geological and engineering
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parameters including area of closure, porosity, thickness, gas saturation, gas formation
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volume factor, recovery factor, trapping mechanism, effective porosity trap fill and
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hydrocarbon accumulation. All of the required data for this assessment have been
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compiled, reviewed and summarized. The Department of Mineral Fuel (DMF), the
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Ministry of Energy, provided these data.
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The FASPU software is a prototype package of programs designed to assess
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the resource potential of undiscovered oil and gas resources (Crovelli and Balay,
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1990). The play analysis is a general term for various geologic models and
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probabilistic methods of analyzing a geologic play for petroleum potential. The play
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analysis is a quantitative approach for estimating undiscovered oil and gas resources
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at a play scale including hydrocarbon source, timing, migration, and potential facies.
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The resource assessment is separated into individual groups that have similar
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geological characteristics and the same type of play attributes. Each play will,
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therefore, be analyzed, including hydrocarbon accumulation in forms of oil, oil with
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dissolved gas, and non-associated gas. The product of these three probabilities is the
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probability that the prospect has a petroleum accumulation, given the play is
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favorable, and called “Conditional deposit probability”. The hydrocarbon volume
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attributes consist of area of closure, thickness of reservoir rock, effective porosity,
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trap fill, depth to reservoir, and hydrocarbon saturation. The hydrocarbon volume
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attributes jointly determine the volume of the hydrocarbon accumulation within the
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prospect. The FASPU software is used in reservoir engineering for calculating the in
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place volumes of oil and non-associated gas.
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As in the 2003 CCOP Technical Secretariat Evaluation report, the GeoX
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software provides a stochastic assessment of resources, risk and economic value.
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CCOP (The Coordinating Committee for Coastal and offshore Geosciences
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Programmers in East and Southeast Asia), 1996 developed this software. The system
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was designed explicitly to support mission critical asset management decisions in oil
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and gas exploration projects. A distinctive model-based architecture working over the
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industry-standard database promotes effective use and integration of data and
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judgment in decision-making. The Monte Carlo simulation produced probabilistic
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estimates of in place resources and recoverable resources based on the estimate of the
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relevant various input. There are six main input parameters as follows.
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1.
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Document: Editing documentation of the context, main attributes of the target
and issues that have been considered or need to be reviewed.
2.
Setup: Establishing global setting for the analysis. These setting involve what
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phases are active, limits, number of trials and seed for the Monte Carlo
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simulation.
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3.
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hydrocarbon pore volume.
4.
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Fluid: Entering estimates of the variables that are used to calculate in place
and recoverable hydrocarbon volumes at surface conditions.
5.
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Volume: Entering estimates of the variables that are used to calculate the
Correlation: Entering estimates of the relevant correlations between volume
and fluid variables.
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Risk: Entering estimates of the chance of adequacy of the different factors that
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control the success of the exploration target and risk.
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The Monte Carlo simulation and probability of success (POS) were applied to
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the resources determination method. The Monte Carlo simulation, shown in Figure 1,
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is a powerful tool for obtaining solutions by numerical methods using random
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numbers and a function of the engineering (McCray, 1975). Random numbers were
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taken from an appropriate range of values repeatedly selected values entering into the
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calculations. Several hundred to several thousand random numbers, trials were
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generally used to obtain suitable results, which made the technique less suitable for
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manual calculations than to the electronic computer. Essentially, the answers could be
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arranged in a form that gave the fractions of total results, which fell within certain
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ranges in this method. Thus, it is readily adapted to solve probability problems and
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constructing probability distribution. Some of the types of solutions that have been
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obtained by simulation are as follows.
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1.
Estimates such as probability distributions of petroleum resources will give
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P90 (Proved), P50 (Proved + Probable) and P10 (Proved + Probable +
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Possible) reserves.
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2.
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Estimates such as probability distributions of rates of return that might be
obtained in the business venture.
3.
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Probability distributions of the possible “state of nature” such as the probable
ultimate recovery from a petroleum reservoir.
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4.
Determine the most profitable number of parallel facilities.
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5.
Determine the probability diagram formed by combining quantities whose
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variation can be expressed algebraically with those whose variations can be
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expressed only empirically or graphically.
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6.
Illustrations of the validity of theorems.
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7.
Sensitivity analysis.
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The probability of success (POS) was used for prospect evaluation (CCOP,
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2000). The proportion of oil and gas in our prospect is usually one of the input
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parameters in the volumetric calculation. However, in some cases we wish to evaluate
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different cases (oil case, oil case and combination case). In these situations, we had to
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assess the risk associated with each case. For any given prospect, the four possible
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outcomes (dry, oil, gas and oil & gas) were independent. The resource distribution or
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range of outcomes being used in the evaluation is a function of the geological risks
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(including hydrocarbon resource, timing, migration, trap occurrence, potential
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reservoir facies, effective porosity and hydrocarbon accumulation) of the project.
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The PPA software will be applied to the Monte Carlo simulation and
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probability of success (POS) of theoretical analysis. Ruksutjaritkul, 2010 created this
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software. In terms of economic evaluation for natural gas potential assessment will be
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performed to find the best Internal Rate of Return and Profit to Investment Ratio. The
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software was developed on Microsoft Visual Basic version 6.0 that is the enterprise
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edition with source code and utility for natural gas potential assessment. The program
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includes necessary command buttons similar to the commercial software such as
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“Main page”, “Calculate”, “Check input, Clear” and “Save & Print”. The software is a
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substitution of common order such as input, output and help. This software includes a
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main page and three modules (including technical, gas production forecast and
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economic consideration) which are shown in Figure 2.
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Results
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Technical Consideration
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In terms of Technical consideration, the natural gas potential assessment of the
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Chonnabot prospect were performed by PPA software are shown in Tables 1 and 2.
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The results can be summarized as follows:
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1. The quantities of gas in place resources for the prospect are 147.49 Bcf
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(Billion cubic foot) at 90%, 405.71 Bcf at 50% and 926.71 Bcf at 10% of
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fractile, respectively.
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2. The quantities of gas recoverable resources for prospect are 132.76 Bcf at
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90%, 365.14 Bcf at 50% and 834.07 Bcf at 10% of fractile, respectively are
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shown in Table 3.
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Economic Consideration
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In terms of economic consideration, the determination of the Internal Rate of
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Return (IRR) and Profit to Investment Ratio (PIR) are analyzed and estimated in all
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case studies. The exploration and production periods under the Petroleum Acts
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“Thailand III” are divided into 3 years of exploration period and 20 years of
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production period. This study production is currently in its 4th year of investment. The
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total exploration period is 3 years and then production period is 23 years, which are
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divided in this study. The following is the work plan schedule:
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1st year @ 2010: Petroleum concession
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2nd year @ 2010: Geological and geophysical surveys
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3rd year @ 2010: Drill exploration, appraisal and production wells
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4th year @ 2010: Production
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The petroleum economic study under the concession system and petroleum
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economic evaluation of Thailand III (ASCOPE/CCOP, 1982) has detailed basic
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assumptions and other assumption costs, which are shown in Table 4 and the
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economic results are shown in Table 5.
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Conclusions
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Technical consideration, the quantities of gas in place resources are 147.49 Bcf at
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90%, 405.71 Bcf at 50% and 926.74 Bcf at 10%of fractile, respectively. The
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quantities of gas recoverable resources are 132.74 Bcf at 90%, 365.14 Bcf at 50%and
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834.07 Bcf at 10% of fractile, respectively.
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The comparison between PPA and FASPU software show that PPA software
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had an error difference from FASPU software of approximately 26.7% because the
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PPA software had been considered in geological parameters, which are net to gross
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ratio and geometric factor. The comparison between PPA and GeoX software, PPA
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software had an error difference from GeoX software of approximately 15.3%.
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Economic consideration, the Internal Rate of Return (IRR) and Profit to
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Investment Ratio (PIR) for natural gas in northeastern Thailand are 20.03% and 1.10.
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The economic results are the best estimate in this case.
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Recommendations
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The research methodology completed and developed by this program is useful but
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some data that were used in the case studies are not proper such as thickness and
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geometric factor data. The gas production forecast module of PPA software was
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developed for planning gas production of each project. The input parameter consists
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of in place or recoverable resource, field gas production rate, efficiency at time of
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constant rate, time of constant rate and production rate decline per year. The number
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of years it is produced and the production rate per year calculates the results of gas
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production forecast module.
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Acknowledgement
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The research presented in this paper was supported by the 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 natural gas potential assessment is also greatly appreciated.
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The authors would like to thank Assoc. Prof. Kriangkrai Trisarn for his valuable
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suggestions.
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References
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ASCOPE/CCOP. (1982). Review on Petroleum data management and Monte Carlo
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simulation. Proceedings of the Joint at ASCOPE / CCOP workshop; Feb 22-
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25, 1982; Jakarta, Indonesia, p. 76.
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CCOP. (2000). Guidelines for risk assessment of petroleum prospects, [On-line].
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Available
from:
www.ccop.or.th/PPM/document/home/
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Accessed date: Jan 11, 2011.
RiskAssess.pdf.
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CCOP. (2003). Evaluation Report on the use of GeoX in the CCOP Member Countries,
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[On-line]. Available from: www.ccop.or.th/download/PPM_GEOX.pdf. Accessed
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date: Jul 15, 2009.
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CCOP. (2006). Petroleum policy and management project. Final Report, Bangkok,
Thailand, p. 68.
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Crovelli, R.A. and Balay, R.H. (1990). FASPU English and Metric version: analytic
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petroleum resource appraisal Microcomputer programs for play analysis using a
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Reservoir-Engineering Model. The Department of the Interior U.S. Geological
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Survey report, p. 15.
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236
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Department of Mineral Fuels. (2007). Petroleum and Coal Activities in Thailand.
Annual Report, Aug 23-25, 2007, Bangkok, Thailand, p. 30.
McCray, A.W. (1975). Petroleum Evaluation and Economic Decisions. 1st ed.
Prentice-Hall, Inc., New Jersey, 448p.
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Simulation Model
- Area
- Thickness
- Percentage oil
or gas
- Recovery
* 1,000
Probability
100
Random Number
(Probability)
Generator of
Expect Value
0
Hydrocarbon
Figure 1. Monte Carlo Simulation Model (ASCOPE/CCOP, 1982)
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Figure 2. The main page of PPA software
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Start
Engineering parameters:
Geology parameters:
- Area of closer
- Thickness
- Porosity
- Gas saturation
- Gas Recovery Factor
- Gas Formation Volume Factor
- Hydrocarbon source
- Timing
- Migration
- Trap occurrence
- Effective porosity
- Hydrocarbon accumulation
- Net/Gross ratio
- Geometric factor
- Trap fill
Distribution
Uniform
Geology parameters:
- Hydrocarbon source
- Timing
- Migration
Triangular
- Trap occurrence
- Effective porosity
- Hydrocarbon accumulation
- Net/Gross ratio
- Geometric factor
- Trap fill
Calculate and compile
In place resources and
Recoverable resources
at F90, F50 and F10
End
Figure 3. Flow chart for technical consideration
ความเป็
(เปอร์ เซ็นต์)
นไปได้ร้อยละสะสม
Cumulative
percentage
15
100%
ความเป็ นไปได้ที่ร้อยละ 90
80%
60%
ความเป็ นไปได้ที่ร้อยละ 50
40%
20%
ความเป็ นไปได้ที่ร้อยละ 10
0%
0
200
400
600
800
1000
1200
1400
1600
Recoverable
resource
ปริ มาณก๊าซธรรมชาติ
ที่สามารถผลิ
ตได้ (พัน[Bcf]
ล้านลูกบาศก์ฟุต)
Figure 4. Natural gas potential assessment of Chonnabot Prospect
by PPA software
1800
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Table 1. Input parameters of Technical consideration (CCOP, 2006)
Value
Parameter: Engineering
Units
Min
Mean
Max
Area of closer
acer
2020
none
12530
Thickness
ft
107
240
374
Porosity
%
3
none
18
Gas saturation
%
53
none
86
Gas recovery factor
%
90
none
90
Gas formation volume factor
-
none
0.0034
0.0032
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Table 2. Input parameters of Technical consideration (CCOP, 2006)
Parameters: Geology
Units
Value
Hydrocarbon source
%
100
Timing
%
100
Migration
%
100
Trap occurrence
%
80
Potential reservoir facies
%
90
Effective porosity
%
100
Hydrocarbon accumulation
%
100
Net/Gross ratio
%
68
Geometry factor
%
86
Trap fill
%
48
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Table 3. Result of Technical consideration for natural gas potential assessment of
Chonnabot Prospect
In place resources/Recoverable resources (Bcf)
Softwares
(P95*)/P90
P50
(P5*)/P10
FASPU (*)
139.78 / 132.79
554.20 / 498.80
2, 081.87 / 1, 873.68
GeoX
124.20 / 111.80
351.90 / 316.80
942.00 / 847.80
PPA
147.49 / 132.74
405.71 / 365.14
926.74 / 834.07
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Table 4. The basic and other assumption cost at the reserves 365.14 Bcf
Value
Unit
4.4
US$/MMBtu
Operation expense
1000
MMUS$
Facility cost
145
MMUS$
Capital cost
276.41
MMUS$
Type:
Gas price
- Geological and geophysical survey
- Drilling explorations well
- Drilling production well
- Pipe line
Income tax
50
%
Discount rate
10
%
Tangible cost
20
%
Intangible cost
80
%
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Table 5. Result of Economic consideration
Results
Gross revenue
Value
1,423.46 MMUS$
Royalty
149.53 MMUS$
Income tax
314.27 MMUS$
Operation cost
378.89 MMUS$
IRR (Internal Rate of Return)
20.03 %
DIRR (Discount Internal Rate of Return)
9.12 %
PIR (Profit to Investment Ratio)
1.10