Uncertainty Assessment for Field Development Study Using Monte Carlo Simulation on Salap Field Multilayer Gas Reservoir

Uncertainty assessment for Field Development Study is often only carried out in a Deterministic Method by only creating scenario sensitivity based on theoretical assumptions without add subsurface risk factors. In the uncertainty assessment model, when a model is made with a variable that only has one value for each sensitivity is called the Deterministic Method. Meanwhile, when a model is made with a variable that has value in the form of a probability distribution, the method is called the Probabilistic Method. In the Probabilistic Method, the probability distribution is influenced by the risk factors, while in the Deterministic Method, these factors have no effect because the input value is only based on theoretical assumption. Uncertainty assessment for the Salap Field Development Study was carried out using the Probabilistic Method Monte Carlo Simulation. The results of the study provide the number of proven reservoirs, volume in place, volume of resources, number of development wells, plateau production period and field life that already accommodates subsurface risk factors and uncertainty in geological-reservoir data. The paper also compares the assessment result between the Probabilistic Method with the Deterministic Method to see how risk factors influenece the study results. Uncertainty assessment in this study is not only carried out probabilistically using Monte Carlo Simulation, but using the same data it is also carried out in a deterministically to provide an overview of the effect of subsurface risk factors on the uncertainty assessment results. field development study is based on uncertainty assessment using the Probabilistic Monte Carlo Simulation accommodates subsurface risk factors and geological-reservoir data uncertainty. The assessment results be used as input in the Petroleum Expert Suites or PETEX Reservoir Simulation software. (GAP – MBAL – Prosper). By using the same input data, uncertainty assessment is also conducted using the Deterministic Method to compare the results with the study.


INTRODUCTION
Salap Field is one of the onshore exploration prospects in the East Java Basin which has not been drilled before. Referring to the results of the seismic interpretation of the Salap Field and the reservoir characteristics of the AGT Field which produce from the samillar basin and formation, the formation target is multilayer gas reservoir with varying reservoir characteristics on each layer. The variation on reservoir characteristics in the analog field has become the basis for conducting an uncertainty assessment in the Salap Field Development Study.
Uncertainty assessment can be interpreted as a risk analysis by conducting an assessment of all uncertain variables to provide a range of success or failure as well as the implications that arise. The range and implications are needed in the decision-making process so that decision-makers have an analytical basis in making choices and can prepare alternative plans that accommodate risk and uncertainty factors.
In the uncertainty assessment model, when a model is made with a variable that only has one value for each sensitivity is called the Deterministic Method. Meanwhile, when a model is made with a variable that has value in the form of a probability distribution, the method is called the Probabilistic MethodIn the oil and gas industry, uncertainty assessment is often only carried out in a deterministic manner by making scenario sensitivity using theoretical assumptions without accommodating subsurface risk factors.
Field Development Studies should be carried out in a probabilistic manner, because the Deterministic Method can sometimes give overly optimistic estimation. Overly optimistic estimation often lead to inappropriate decision-making on planning for field development.
Monte Carlo simulation is one of the probabilistic methods that can be used to estimate the quantitative value of a model by utilizing the random number that is entered in the input of the calculation model. In Monte Carlo Simulation, a model is built with all input variables in the form of probability distribution data that correlated with random values from random numbers. The Monte Carlo simulation will perform a calculation simulation repeatedly with each input variable that changes according to a random value that appears. Iterations can be done up to thousands of times depending on the model complexity. The simulation will provide calculation results with many output values and forming a qualitative pattern that has a tendency to any value so that it can be interpreted quantitatively using the selected probability.
The topic of uncertainty assessment has been widely raised in journals that discuss field development study in the scope of development and exploration. Most of the researcher that discusses uncertainty assessment only mentions the advantages of the Probabilistic Method when compared to the Deterministic Method, without comparing the assessment results of the two methods using the same data.

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Uncertainty assessment in this study is not only carried out probabilistically using Monte Carlo Simulation, but using the same data it is also carried out in a deterministically to provide an overview of the effect of subsurface risk factors on the uncertainty assessment results.
The purpose of this research is to conducting a field development study which is based on uncertainty assessment using the Probabilistic Monte Carlo Simulation Method that accommodates subsurface risk factors and geological-reservoir data uncertainty. The assessment results will be used as input in the Petroleum Expert Suites or PETEX Reservoir Simulation software. (GAP -MBAL -Prosper). By using the same input data, uncertainty assessment is also conducted using the Deterministic Method to compare the results with the study.
The purpose of this study is to provide study results in the form of PETEX Reservoir Simulation predictions which includes estimation of:

II. METHODS
The methodology that used are: To focus the objectives of the study, the following are the scope and limitations that will be applied in the paper, 1. Hydrocarbon prospect of the Salap Field assumption will be referred with the analog field, which is dry gas without any impurities; 2. The marker depth data for each reservoir refers to the results of the seismic interpretation of the Salap Field; 3. The pressure and temperature data for each reservoir layer is calculated using the normal gradient at the marker depth from the seismic interpretation of the Salap Field; 4. Reservoir characteristics, fluid and formation transmissibility data is using analog field data; 5. Data uncertainty assessment is only applied on Geology-Reservoir data variables such as Bulk Volume (Vb), Net to gross (NTG), Porosity, Water Saturation (Sw), Net Pay (h) and Permeability (k); 6. Assumption of probability distribution for Volume Bulk (Vb) and Net to gross (NTG), is using Uniform distribution. Distribution type selection is based on analog field data distribution; 7. Assumption of probability distribution for Porosity and Water Saturation (Sw), is using Triangular distribution. Distribution type selection is based on analog field data distribution; 8. Assuming the probability distribution for Net Pay (h) and Permeability (k) is using the Discrete Binomial distribution. Distribution type selection is based on analog field data distribution; 9. The reservoir simulation software that used is Petroleum Expert Suites or PETEX (GAP-MBAL -Prosper).
Reservoir Model Description:

Reservoir Model:
The reservoir model is assumed to be a Tank Model that created using the MBAL feature in GAP. There are 4 reservoir models for Zones C, D, E and F.

Formation Productivity Model:
The formation productivity (Inflow Performance Relationship) model was created by using the Prosper model of the "Backpressure Method C and n". There are 40 formation productivity models that represent the IPR in Zones C, D, E and F for each well.

Production Well Model:
The production well model is made with the assumption of a multilayer well completion equipped with Inflow Control Valve (ICV), Sliding Sleeve Door (SSD) and Stradle Packer so that the reservoir can be produced either commingly or selectively. There are 10 productions well models of, from SL-01 to SL-10.

Assumption of Surface Facility:
Surface facility is assumed to be 1 junction manifold as Gas plant and a single 8" diameter header with 8 KM length and 1 Buyer Sales point with plant pressure 100 Psig. Upstream junction is 10 production wells in the Salap Field. While the dowstream is a header to the sales point.

Prediction Simulation: Prediction simulation starts on January 1 st 2025 and ends on January 1 st 2061 (End of PSC).
Simulation time step is made per 1 year.

Well/Zone Opening Schedule:
Opening and closing schedule works automatically using the open server feature in the GAP Simulation. The open server command string will control the number of wells and the number of zones that needed to achieve/maintain the total field production target. Even though the model wells are made of 10 wells, in the initial conditions only 1 well and 1 reservoir zone are open so that the number of open wells/zones will be controlled by the gas production target and the performance of each reservoir/well. Field development timeline assumption:
 Preparation of POD Salap Field.
 Preparation of Salap Field Sales Contract with buyers (2 MMSCFD for 5 years).
 Salap Field Sales Contract approval  Drilling 1 Development well.

Phase #3 (2025 -2030)
 Started gas sales to buyers with a sales target of 2 MMSCFD.
 Development Drilling Campaign Salap Field based on POD approval.
 Amendment of Salap Field Sales Contract (Ramp up sales from 2 MMSCFD to 20 MMSCFD for 5 years)
 Continue produces field up to the end of PSC contract

Uncertainty Assessment for Salap Field Development Study
There are 2 calculations that are simulated with the Monte Carlo Simulation, which are the calculation of Gas in Place and the calculation of transmissibility as input for the formation productivity model. In the calculation of Gas In Place the modeled input variables are Bulk Volume, Net to Gross, Porosity and Water Saturation on each reservoir Zone C, D, E and F. In the calculation of transmibility/formation productivity the modeled input variables are Net pay and Permeability on each reservoir Zone C, D, E and F.
The random number value is created by utilizing the RAND function feature in Microsoft Excel. The RAND function placed in a cell in the excel sheet will generate a random number between 0 to 1 for each iteration.
In the oil and gas exploration stage, subsurface risk factors are estimated by evaluating the parameters of the petroleum system of prospect's target. These parameters include: Each parameter has a percentage probability of success or failure with a scale of 0% to 100% according to the completeness of the data and supporting concepts. Furthermore, the probability percentage of each parameter is multiplied to obtain the Probability of Geology (Pg), Geological Chance Factor (GCF) and Risk factor values.
The probability distribution of Volume Bulk and Net to Gross data for each reservoir is made using a uniform distribution type. The lower and upper limits of the distribution use the minimum and maximum values from the analysis of the centered scale data.
The random number generated by the random number becomes one of the inputs in the uniform distribution formula, so that the Volume Bulk and Net to Gross values are the results of the probability distribution that are correlated with the random numbers that appear. The probability distribution of Porosity and Water Saturation data for each reservoir is made using a triangular distribution. The lower, middle and upper values of the distribution use the minimum, mean and maximum values from the analysis of the centered scale data.
The random number generated by the random number becomes one of the inputs in the triangular distribution formula, so that the Porosity and Water Saturation values are the results of the probability distribution that are correlated with random numbers that appear. The probability distribution of the Net pays and Permeability data for each reservoir is made using a discrete distribution type. The lower, middle and upper values of the distribution use the minimum, mean and maximum values from the analysis of the centered scale data.
In the distribution of the permeability data, 1 permeability value of 0.1 mD is added as a value that represents the nonflowing and dry condition of the reservoir to accommodate subsurface risk factors that may occur.
The random value generated by the random number becomes one of the inputs in the discrete distribution formula so that the Net Pay and Permeability values are the results of the probability distribution that are correlated with random numbers that appear.

Monte Carlo Simulation Result
In the Monte Carlo simulation model, subsurface risk factors are included in the permeability calculation model that has been discussed previously. In this study, the percentage of the permeability value that appears 0.1 mD/ Reservoir does not flow/dry is assumed to be 50% which used the Risk Factor that given by Geologist.
The number of iterations of the Monte Carlo simulation is determined by calculating the standard deviation value of the tested variables, determining the maximum error percentage value and the error value of the model and calculating the number of iterations required for the simulation with the standard deviation target to the calculated error value.
The results of the calculation of the iteration number show that to get a model with an error percentage below 3%, a minimum iteration that must be run is 10,000 iterations.
After all the data has been prepared, the Monte Carlo Simulation process is simulating for 10,000 iterations. The results of running create an output in the form of 10,000 Gas In Place values, each iteration number has an input variable with non-uniform categories in each reservoir C, D, E and F which depend on the random number that appears. This allows permutations between the minimum, mean and maximum variables in each simulation.

Figure 2. Monte Carlo Simulation Result on Gas In Place Calculation
From 10,000 iterations on Gas in Place calaculation, an analysis is required to categorizes Gas in Place into P90, P50 and P10 Scenario based on the percentile value of the data on the CDF plot. The value of Gas in Place will be used to determine  Table 7. Iteration Selection as Input to to Simulation Probabilistic Case

Reservoir Simulation Using Probabilistic Uncertainty Assessment Data Input (Salap Field Development Study)
The Probabilistic Case Simulation is divided into 3 scenarios, which are P90, P50 and P10 scenarios. All variables are input based on the data on the iteration number that has been categorized previously. In contrast to the Deterministic Case, the input variables in the Probabilistic Case have varied categories, thus allowing permutations between the minimum, mean and maximum variables. In the Probabilistic Case Scenario P10, all the variables inputted in the simulation use the variables in Iteration Number 4500, both the bulk Volume, Net to Gross, Porosity and Water saturation variables for the calculation of Gas in Place as well as the Net pay and permeability variables for the calculation of transmibility/formation productivity on Zone C, D E and F respectively. Transmibility -Probabilistik

Figure 5. Probabilistic Case Cummulative Profile Forecast
The Probabilistic Case Scenario P90 simulation result shows that the field can produce 20 MMSCFD for 1 year with the required number of development wells as many as 10 producing wells from Reservoir D. Total resources at the end of the simulation are 10.2 BCF (RF 93%) with Field Life producing above 1 MMSCFD (technical limit) for 6 years. Remaining resources in this scenario are included in the marginal category at the end of the simulation.
The Probabilistic Case Scenario P50 simulation shows that the field can produce 20 MMSCFD for 3 years with the required number of development wells as many as 10 producing wells from Reservoir D and F. Total resources at the end of the simulation are 45.6 BCF (RF 80%) with Field Life producing above 1 MMSCFD (technical limit) for 16 years. Remaining resources in this scenario are included in the marginal category at the end of the simulation.
The Probabilistic Case Scenario P10 Simulation shows that the field can produce 20 MMSCFD for 5 years with the required number of development wells as many as 10 producing wells from Reservoir C, D, E and F. Total resources at the end of the simulation are 77.3 BCF (RF 77%) with Field Life production above 1 MMSCFD (technical limit) for 26 years. Remaining resources in this scenario are included in the marginal category at the end of the simulation.  In the Deterministic Case Low Scenario, all variables entered in the simulation use the minimum value of the centralized data scale analysis, both the bulk Volume, Net to Gross, Porosity and Water saturation variables for the calculation of Gas in Place as well as the Net pay and Permeability variables for the calculation of transmibility/formation productivity on each reservoir Zone C, D, E and F.
In the Deterministic Case Mid Scenario, all variables inputted in the simulation use the mean value of the centralized data scale analysis, both the bulk Volume, Net to Gross, Porosity and Water saturation variables for the calculation of Gas in Place as well as the Net pay and Permeability variables for the calculation of transmibility/formation productivity on each reservoir Zone C, D, E and F.
In the Deterministic Case High Scenario, all the variables inputted in the simulation use the maximum value of the centralized data scale analysis, both the bulk Volume, Net to Gross, Porosity and Water saturation variables for the calculation of Gas in Place as well as the Net pay and Permeability variables for the calculation of transmibility/formation productivity on each reservoir Zone C, D, E and F.  The Deterministic Case High Scenario Simulation result shows that the field can produce 20 MMSCFD for 13 years with the required number of development wells as many as 2 producing wells from Reservoir C, D, E, and F. Total resources at the end of the simulation are 188 BCF (RF 69 %) with Field Life producing above 1 MMSCFD (technical limit) for 36 years. The RF value shows that Remaining Resources is still high at the end of the simulation.

Comparison of Total Gas in Place
In the estimated of Total Gas in Place Total Reservoir C, D, E and F, Case Deterministic shows the estimated Total Gas in Place range are 23 -273 BCF. In the other hand, in the Probabilistic Case, the estimated range for Total Gas in Place are 10 -100 BCF.

Comparison of Development Wells
In the required development wells to achieve the sales target of 20 MMSCFD, Case Deterministic, shows that the field requires 10 development wells in the Low Scenario, 3 development wells in the Mid Scenario and 2 development wells in the High Scenario. In the other hand the Probabilistic Case shows that the field requires 10 development wells in all Scenarios P90, P50 and P10.

Comparison of Plateau Production Period
In the 20 MMSCFD plateau production period comparison, Case Deterministic shows that the plateau production lasts for 1 year for the Low Scenario, 7 years for the Mid Scenario and 13 years for the High Scenario. In the other hand, in the Probabilistic Case, the plateau production lasts for 1 year for the P90 scenario, 3 years for the P50 scenario and 5 years for the P10 scenario.

Comparison of Field Life
In comparison of field life on produces above 1 MMSCFD (  Simulation that allows permutations of variable categories in each simulation. There is a subsurface reservoir dry risk factor which is also included in the simulation. With influence of risk factor, the Salap Field is estimated to have the potential Gas in Place 11 -100 BCF, Gas Resources 10 -77 BCF, the number of development wells required are 10 wells, plateau production period are 1-5 years and field life above technical limit are 6-26 years. 2. Uncertainty assessment also conducted using Deterministic method as comparison. The uncertainty assessment Deterministic Case of the Salap Field is discrete and there are no permutations of variable categories in each simulation. No risk factors for subsurface reservoir dry were included in the simulation. Without these risk factors, the Salap Field is estimated to have the potential Gas in Place 23 -273 BCF, Gas Resources 19 -188 BCF, the number of development wells required are 2 -10 wells, plateau production period are 1-13 years and field life above technical limit are 10-36 years. 3. The Deterministic Method sometimes gives overly optimistic study results than the Probabilistic Method in all aspects. This is because the assessment of uncertainty data is limited and there is no risk of subsurface reservoir dry that included in the study. 4. Field Development Studies should be carried out probabilistically, because the Deterministic Method can sometimes provide overly optimistic estimation. 5. Overly optimistic estimates often lead to inappropriate decision-making in field development planning and could be mislead the development strategy. 6. Beside due to the output of the Deterministic Method which tends to be overly optimistic, the Probabilistic Method is recommended because it can perform a broader assessment of data uncertainty and subsurface risk than the Deterministic Method. So that all aspects of uncertainty and risk factors can be accommodated in the simulation.