A NEW SIMULATION OPPORTUNITY INDEX BASED SOFTWARE TO OPTIMIZE VERTICAL WELL PLACEMENTS

A NEW SIMULATION OPPORTUNITY INDEX BASED SOFTWARE TO OPTIMIZE VERTICAL WELL PLACEMENTS FINAL PROJECT By: WARDANA SAPUTRA 12211031 Submitted in partial fulfillment of the requirements for BACHELOR OF ENGINEERING DEGREE at the Department of Petroleum Engineering DEPARTMENT OF PETROLEUM ENGINEERING FACULTY OF MINING AND PETROLEUM ENGINEERING BANDUNG INSTITUTE OF TECHNOLOGY 2015

A NEW SIMULATION OPPORTUNITY INDEX BASED SOFTWARE TO OPTIMIZE VERTICAL WELL PLACEMENTS By: WARDANA SAPUTRA 12211031 Submitted in partial fulfillment of the requirements for BACHELOR OF ENGINEERING DEGREE at the Department of Petroleum Engineering Faculty of Mining and Petroleum Engineering Bandung Institute of Technology Approved by: Final Project Advisor, On Prof. Ir. Tutuka Ariaji, M.Sc., Ph.D.

A New Simulation Opportunity Index Based Software to Optimize Vertical Wells Placements W. Saputra*, T.Ariaji**; Institut Teknologi Bandung Copyright 2015, Institut Teknologi Bandung This paper was prepared in partial fulfillment of the requirements for Bachelor of Engineering Degree at the Department of Petroleum Engineering, Institut Teknologi Bandung. This paper was written using standard SPE Publication Style in 2015. Abstract Profitability of investment projects for oil or gas field development is dropping drastically in the period of low oil prices. The ability to cost-effectively achieve highest recovery of the field makes good business sense whether the oil prices is high or low. The profitability of a project highly depends on the effectiveness of the field development scenario which is related to the selection of well locations. The appropriate well locations would yield maximum sweep efficiency and recovery. Generally, engineers are still using the conventional trial and error method to determine well location by manually looking for the highest recovery after performing reservoir simulations. Thus, this conventional method which requires intuitive reservoir engineering diagnosis could be highly time and cost consuming process. To overcome these issues, a new software program SOI Tool has been developed to optimize the selection of appropriate well locations based on Simulation Opportunity Index (SOI). SOI is an intelligent method to identify zones with high potential to production, which with conventional reservoir engineering often represents cost-effectiveness and time consuming problems. The producer potential of an area is empirically calculated based on basic rock and fluid properties and reservoir pressure as its energy capacity. By applying SOI into this compact software, it is possible to rank a number of best layers in each X-Y location vertically resulting the appropriate vertical well locations of multilayer reservoirs. In order to obtain best results, the original SOI formula (Molina et al., 2009) has been modified into the new SOI formula for oil field and gas field. Hence, the developed software can be applied either for a green oil field or gas field. Furthermore, for infill purposes of a mature field, the developed software has also a competency to consider existing wells effect. This paper shows the success of developed software in obtaining the optimal solution with better accuracy in resulting highest recovery and indeed with less time and less effort than the conventional trial and error method. Keyword: Simulation Opportunity Index, Software, Cost-effective, Well Placements, Existing Wells, Multilayer * Student of the Department of Petroleum Engineering Institut Teknologi Bandung ** Final Project Advisor and Lecturer of the Department of Petroleum Engineering Institut Teknologi Bandung Introduction In this age of low oil prices, increasing well costs, and maturing reservoirs, engineers have been constantly challenged by the demanding ask to provide fast, accurate, and cost-effective field development plans. The most decisive factor in field development to achieve the highest hydrocarbon recovery is the selection of well locations. Yet, to determine an appropriate well location is not an easy way because of so many reservoir parameters which may affect the drainage performance. For an illustration, suppose there are three locations in an oil field; A, B, and C. Location A has the highest oil saturation with low porosity and permeability, Location B has the best value of porosity and permeability, whereas Location C has the highest pressure potential. The question is which drilling locations will result the highest oil recovery. To find the answer of those issues, mostly engineers use the conventional method of trial and error by picking a random point location then do reservoir simulation on it. The recovery factor which is obtained from simulation then compared with see who the best among them. It looks like an easy way to do, but imagine that a reservoir model consists of the thousands points. How many well locations should be simulated? Indeed, this conventional trial and error method is highly time and cost consuming process. SOI Tool, a new petroleum engineering software has been developed as the simple, fast, and accurate way to obtain the most appropriate well locations. The main idea behind SOI Tool software development is the utilization of existing static and dynamic simulation models to generate a new 3D reservoir property called Simulation opportunity Index (SOI) as an accurate indicator to determine the sweet spots areas with the maximum flow and storage capacities. In this software, SOI formula has been modified to accommodate either oil or gas field. An algorithm to avoid the presence of existing wells has also been added for infill purposes. In order to verify the pertinence of the developed software, this SOI Tool has been already validated through examples of field case studies in Natuna and Madura Offshore either for new drilling purposes of green field or infill purposes of brown field.

2 Wardana Saputra - 12211031 Fig. 1 Welcome screen of SOI Tool A new petroleum engineering software to optimize the selection of well locations based on Simulation Opportunity Index. Simulation Opportunity Index Simulation Opportunity Index (SOI) is an intelligent method that uses numerical simulation models to identify zones with high potential of production, which with conventional reservoir engineering often represents a too complex and time consuming problem (Molina et al., 2009). The producer potential of an area is calculated based on the basic rock and fluid properties, as well as the energy capacity associates with the reservoir pressure. All those parameters are combined and normalized to generate an index between 0 and 1, capable to identify the zones of greater potential. To calculate this index, three main variables are considered. First, the movable hydrocarbon capacity, defined as the difference between current saturation and residual saturation while considering porosity as pore volume capacity. Second, the flow capacity of reservoir, which combines the net thickness of the cell, permeability, and hydrocarbon mobility (kh/µ). The third is pressure potential as well as the energy capacity of the reservoir, defined as the difference between current reservoir pressure and the abandonment pressure. In this software, the original SOI formula that was introduced in 2009 has been modified in order to achieve more accurate indexes. For oil field, the modification has been done by introducing the new three indexes with equal weightage: Movable Oil Index (MOI), Oil Flow Index (OFI), and Pressure Potential Index (PPI). The mathematical definitions of these terms are defined as follows: MOI OFI PPI S S ( ) S o or 1/ 2...(1) kh ( ) or 1/ 2...(2) o P P ( P abn 1/ 2...(3) abn ) The normalized of geometric mean of these indexes would then yield the dimensionless Simulation Opportunity Index, SOI, as follows: SOI 3 ( MOIxOFIxPPI )...(4) Originally, this SOI method was developed as a mapping method. The SOI parameters can be easily calculated by utilizing current reservoir engineering simulator to generate a new properties map SOI Map as shown in Fig. 2. At first MOI, OFI, PPI 3D properties is created then normalized resulting 3D SOI properties. In order to generate 2D SOI Map, all SOI value of all layers in a certain X-Y location is summed by this following equation: cum SOI n k 1 SOI ( i, j, k )...(5) The utilization of this map is by looking for the highest SOI value (indicated by red color) which is convinced as the highest potential to production area. This main concept of SOI Map will be used to develop algorithm of SOI Tool software. Henceforth, this part will be discussed later in software development part.

3 Wardana Saputra - 12211031 Movable Oil Index (MOI) Oil Productivity Index (OFI) Pressure Potential Index (PPI) Simulation Opportunity Index (SOI) So Sor ( ) S or 1/ 2 kh ( ) o 1/ 2 P P ( P abn ) abn 1/ 2 X X SOI 3 ( MOIxOFIxPPI ) Fig. 2 Workflow of Simulation Opportunity Index as a Mapping method SOI Map. SOI Formula for Gas Field In the gas reservoir, fluid properties such as density and viscosity are strongly affected by pressure changes in reservoir due to its high compressibility. Thus, a more thorough treatment should be applied to correct gas flow equation in porous media due to these phenomenon. A rigorous concept of gas flow equation is already defined (Al-Hussainy,1965) 2, known as pseudo-pressure, ψ, sometimes called the real gas potential. By using ψ approach, the variation of µ and Z with pressure can be accommodated. The ψ, pseudo-pressure, is defined as: P p dp Z 2... (6) P o The integral term of ψ can be simply calculated by numerical algorithm in SOI Tool software. While the µ and Z is also numerically calculated by Carr-Kobayashi-Burrows gas viscosity (1954) 3 and Brill-Beggs Z-factor (1974) 3. The substitution of this ψ and several empirical-based modifications of SOI equations (1), (2), (3), and (4) yield new three following indexes: Movable Gas Index (MGI), Gas Flow Index (GFI), and Pseudo-pressure Potential Index (PPI). The mathematical definitions of these terms are defined as follows: MGI GFI PPI S S g gr 1/ 2 ( )...(7) S g S gr kh log( )... (8) ( g abn 1/ 2...(9) abn ) The normalized of geometric mean of these indexes would then yield the dimensionless Simulation Opportunity Index for Gas Field, SOIG, as follows: SOI G ( MGIxGFIxPPI...(10) 3 ) Methodology The methodology which was done for the selection of the appropriate well locations is shown by Fig. 3. Fig. 3 Flowchart of Methodology.

4 Wardana Saputra - 12211031 Literature Study. A rigorous understanding of the basics of Simulation Opportunity Index and the syntax of the programming language are necessary. Hence, literature study as the first phase of the methodology was already conducted. This phase builds the foundation of the study in order to result an accurate and comprehensive software development. Data Gathering. In this paper, there are three real fields which were used for case studies. The first case study is Field X which represents an oil green field. Field X is located to the western side of Natuna Sea east blocks and considered as a green field due to there is no existing wells drilled in this blocks. Initialization of existing reservoir models shows original oil in place value of 28.19 MMSTB. The second is Field Y which represents a gas green field. Field Y is also an offshore field located to southern side of Madura Island and considered as a gas field with original gas in place value 128.33 BSCF. And the third is Field Z which represents an oil brown field. As same as Field X, Field Z is also located to the western side of Natuna Sea, but its reservoirs are precisely located in the main block which has bigger original oil in place value of 167.67 MMSTB. Field Z has 26 existing wells as shown in Table 1. while the production commenced in 1979 until 2009, so that it is considered as a mature or brown field. Table 1 Existing Wells of Oil Field Z. Well Name X-location Y-location (i unit) (j unit) A-1 88 19 A-2 81 23 A-3 92 21 A-4 78 17 A-5 71 15 A-6 86 6 A-7 71 23 A-8 83 14 A-9 96 14 A-10 103 19 A-11 77 10 A-12 86 21 B-1 44 19 B-2 37 26 B-3 35 18 B-4 54 21 B-5 28 16 B-6 61 18 B-7 41 13 B-8 49 24 B-9 35 23 B-10 58 24 B-11 24 20 B-12 62 15 B-13 55 18 B-14 48 16 The complete reservoir model specifications of these three case studies are shown in Table 2. All of these data combined with reservoir properties grid data (GSLIB ASCII) are needed as required input for SOI Tool software program. Then based on these input, developed software will automatically search for the appropriate well locations. Black-oil reservoir model simulator is used in all of the simulation processes to validate the results. Table 2 Specifications of Case Studies. Case Study-1 Case Study-2 Case Study-3 Field Name Field X Field Y Field Z Reservoir Fluid Type Oil Gas Oil Existing Wells No No Yes Abandonment Pressure 1500 psia 100 psia 1500 psia Residual Saturation 0.2 0.2411 0.2 Average Reservoir Temperature 180 o F 180 o F 180 o F Average Fluid viscosity 0.941 cp 0.85 cp 0.941 cp Specific Gravity 0.86 0.7 0.86 H 2S Content - - - CO 2 Content - 0.1 - N 2 Content - - - Reservoir Model Grid Size (i x j x k), grid unit (28 x 39 x 90) (82 x 69 x 40) (105 x 31 x 45)

5 Wardana Saputra - 12211031 Software Development. Development of SOI Tool used the latest programming language, C# (pronounced as see sharp). C# is a multi-paradigm programming language encompassing strong typing, imperative, declarative, functional, generic, object-oriented (class-based), and component-oriented programming disciplines. It was developed by Microsoft within its.net Framework initiative. C# is one of the programming languages designed for the Common Language Infrastructure. C# is intended to be a simple, modern, general-purpose, object-oriented programming language. Its development team is led by Anders Hejlsberg. The most recent version is C# 5.0, which was released in August 2012. These are some reasons to utilize C# as a main programming language in SOI Tool development: C# is new simple object oriented programming language which will result the programs with the best user interface. Although C# is a new programming language, it is relatively easy to learn due to its similarity to that of other C-style languages such as C, C++ and Java. In particular. Moreover it made SOI Tool has a great compatibility. C# support for software engineering principles such as strong type checking, array bounds checking, detection of attempts to use uninitialized variables, and Math library. By combining C# as core programming language with GSLIB ASCII for reservoir grid properties input, the SOI Tool can be an extremely fast computing software. Indeed, it is less cost and time consuming. GSLIB ASCII. In order to read whole 3D reservoir grid properties with high effectiveness and less time consuming, SOI Tool was developed to read the most common exported file of any existing reservoir simulation software (e.g. Petrel, CMG, etc.), known as GSLIB ASCII. GSLIB is an acronym for Geostatistical Software Library which this name was originally used for a collection of geostatistical programs developed by students and faculty at Stanford University over last 15 years (Clayton et al., 1990). This GSLIB is a type of ASCII file which is suitable to use in such a software development. Fig. 5 shows an anatomy of GSLIB ASCII file which can be opened by any text reader software. File Name Number of columns Columns names Space delimited data Fig. 4 GSLIB ASCII File Data Example Porosity. SOI Tool was developed into some integrated parts as shown in Fig. 5. They are Field Data, Grid Data, Existing Wells, Drainage Plan, and then Results. Fig. 5 Home Page of SOI Tool.

6 Wardana Saputra - 12211031 In general, the process of SOI Tool is to determine the grid coordinates of a reservoir model. In order to reach the most effective results, a new algorithm program is proposed for SOI Tool which the flowchart is represented by Fig. 6. Start Input Data P>P abn S at>s atr No Eliminate Cells Yes Generate Potential Cells Population Oil Field Type Gas Generate Pseudo- Pressure function MOI OFI PPI MGI GFI PPI Calculation of SOI SOI SOI g Normalize SOI SOI> Filter No Eliminate Locations Yes Generate Potential Locations Pop. Sort Best Layers, Calculate Cum.SOI in Each Locations Normalize Cum. SOI Existing Wells Yes No Output Best Locations Sort Locations Depending on Well Spacing Sort Locations Depending on Existing Wells End Fig. 6 Flowchart of Developed Software SOI Tool.

7 Wardana Saputra - 12211031 Field Data Part. This is the first data group should be filled completely. It is a must to make clear the reservoir fluid type since SOI method is different between oil and gas field resulting the difference of oil field and gas field required input. As mentioned before, gas is strongly affected by pressure changes in reservoir due to its high compressibility. Gas composition or specific gravity is needed to correlate viscosity against pressure then generate pseudo-pressure table to advance SOI calculation of gas reservoir. Fig. 7 Field Data Part of SOI Tool (Example: Gas Field Y). Grid Data Part. In this part, users should fill reservoir grid coordinates properly. Please note that grid coordinate is in grid unit (i, j, and k) and it can be simply found in any reservoir simulation models. After completely filling grid number, the next step is import reservoir grid properties into this software. They are: thickness, net to gross, permeability, porosity, pressure, and saturation of hydrocarbon. As mentioned before, all of these data must be in GSLIB ASCII file, which is easily able to import from any existing reservoir simulation software. Fig. 8 Grid Data Part of SOI Tool (Example: Gas Field Y). Existing Wells Part. SOI Tool also has competency to consider the effects of existing wells. If previous wells actually exist in a field and the software does not take it into account, an error may occur in the selection of infill wells. The drainage of the new wells may overlap the drainage of the previous wells so that their productivity will drop. Hence, in this section the users should choose whether existing wells do exist or not. Developed software could generate table of existing wells, and users are allowed to input the location in grid unit as shown in Fig. 9. Fig. 9 Existing Wells Part of SOI Tool (Example: Oil Field Z).

8 Wardana Saputra - 12211031 The grids which are located near the existing wells would be marked by the software depending on the well spacing of each well. These marked grids are less prioritized in the process of ranking, thus their SOI value would be lowered. Drainage Plan Part. This part is the main control of results. Users are allowed to input the number of proposed wells and proposed layers that they need. This option accommodate a proper results for multilayer reservoirs. The majority of the fields nowadays are produced by multilayer reservoirs which the layers vary in rock properties and resulting different productivity. The location selections of multilayer reservoir wells are more complex than that of single reservoir wells. As shown in Fig.10, for example the reservoir has 40 layers (grid k=40) and only 12 layers are proposed to be perforated. Then the software will search for the best 12 layers of each locations before cumulate its SOI value and get the ranking. Surely this needs a complex algorithm development. This feature would be favorable when a company only has limited budgets to perforate certain numbers of layers. Fig. 10 Drainage Plan Part of SOI Tool (Example: Oil Field X). In this section, the users are also allowed to calculate well spacing of the proposed wells in grid unit. Changing SOI filter value is not too recommended. After completely filling all data, run button is enable to see the results immediately. Results Part. The software simulation results would be shown in this part. As shown in Fig. 11, the results of proposed wells is presented in a table which rank the proposed wells locations with the corresponding best layers and the normalized SOI value. If SOI value equals to 1, the corresponding location should has the maximum cum. SOI value among all potential locations population. Yet, SOI value which is less than 1 sometimes could be chosen as top ranks due to the effect of existing wells which probably marks the nearest area with maximum SOI value as low rank. Fig. 11 Results Part of SOI Tool (Example: Oil Field Z). SOI Tool could show these SOI value into an attractive histogram chart as shown in Fig. 12. to see how big the variances of SOI value among the ranks. Fig. 12 Chart of Results SOI value (Example: Oil Field Z).

9 Wardana Saputra - 12211031 SOI Tool could also show an illustration map of the proposed wells and existing wells as shown in Fig.13. to see the distribution of proposed and existing wells in current field. Fig. 13 Map of Results (Example: Oil Field Z). In addition, either this map or chart could be saved as an image. The proposed wells table also could be saved as an Excel file. Validation. In order to verify the accuracy of SOI Tool, a validation phase is required. The validation phase utilizes a reservoir simulator to obtain recovery factor values of the proposed wells which are the results of SOI Tool computations. As stated before, the validation process uses three case studies as shown in Table 2. The first case aims to validate SOI Tool in selecting optimum well locations of a green oil field (Field X). Since the conventional of trial-error method and the previous studies only utilized 3 proposed wells and 12 layers for each, to make the results comparable, this study also searched for 3 optimum well locations with its best 12 layers to be perforated. The second case aims to validate SOI Tool in selecting optimum wells location of a green gas field (Field Y). Both conventional of trial-error method and this study proposed 3 wells with 20 layers to be perforated for each. This case should also examine the modification of SOI formula for gas field. The third case aims to validate SOI Tool in infill new wells of a brown oil field (Field Z). This case also aims to examine how good SOI Tool in considering the presences of 26 existing wells in this field and indeed to test whether these infill wells can increase the oil recovery. Table 3 SOI Tool Parameters Used in Validation Phase. Case Study-1 Case Study-2 Case Study-3 Number of Proposed Wells 3 3 4 Number of Proposed Layers 12 20 12 SOI Filter 0.5 0.5 0.4 Summary of designed parameters of SOI Tool which were used in this validation is shown in Table 3. And Table 4. Table 4 Reservoir Simulation Parameters Used in Validation Phase. Case Study-1 Case Study-2 Case Study-3 Tubing Size 7.5 inch 7.5 inch 7.5 inch Simulation Control BHP Field Gas Rate BHP Minimum Bottom Hole Pressure (BHP Target) 1500 psia 100 psia 1200 psia Field Production Control - 20 MMscfd 2000 STB/day Production Economic Limit 5 STB/day - 5 STB/day Maximum Water Cut 0.98-0.98 Simulation Period 20 years 30 years 20 years Analysis. After through the validation phase, the analysis phase is the last important phase to determine whether this software needs more improvements or not. SOI Tool has to pass the initial quest to obtain the optimum well locations which give the highest recovery surpassing the previous studies and indeed conventional trial-error results. Result and Discussion Case Study-1 (Green Oil Field: Field X). Previously, there were two studies applied for this field with the same goals to obtain the optimum well placements of Field X. Both of them used the Genetic Algorithm, an algorithm to search for the best individual of given population based on natural selection mechanism, in developing software algorithm. Regrettably, both of objective functions were just simply calculated by multiplying all grid properties such as permeability, porosity, saturation, and pressure. Then SOI Tool comes with an idea to replace this simple objective function with the more sophisticated one, SOI formula. The determination of well locations for this Field X using conventional trial and error method results in the well placements as shown by Table 5.

10 Wardana Saputra - 12211031 Table 5 Well Placements Using Conventional Trial and Error Method Field X. Well X Y Perforated Layers Well 1 19 23 5,6,7,8,9,10,20,21,22,23,24,25 Well 2 9 23 5,6,7,8,9,10,20,21,22,23,24,25 Well 3 7 28 5,6,7,8,9,10,20,21,22,23,24,25 Table 6 shows well placements results using previous study-a. The previous study-a only used the Genetic Algorithm in determining the X-Y location and not the layers which are going to be perforated. Therefore, it can be seen that the conventional and previous study have the same perforated zones. These zones are determined by the estimation and intuition of the reservoir engineers which may result in human error and wrong solution. Table 6 Well Placements Using Previous Study-A (Genetic Algorithm) Field X. Well X Y Perforated Layers Well 1 23 25 5,6,7,8,9,10,20,21,22,23,24,25 Well 2 7 28 5,6,7,8,9,10,20,21,22,23,24,25 Well 3 12 31 5,6,7,8,9,10,20,21,22,23,24,25 Previous study-b improved previous study-a to consider multilayer reservoirs. As shown in Table 7, this study results the different perforated layers which was automatically selected by previous GA software. Table 7 Well Placements Using Previous Study-B (Genetic Algorithm multilayers) Field X. Well X Y Perforated Layers Well 1 12 31 1,3,4,5,6,7,8,9,11,12,15,19 Well 2 7 27 5,6,7,21,24,25,26,28,31,32,36,49 Well 3 22 26 3,4,5,6,7,10,11,12,13,14,19,20 At last, SOI Tool comes to accomplish the previous study-a and previous study-b by considering multilayers and improving the objective functions. The steps by steps how to use SOI Tool to solve this case study-1 are shown in Appendix A. The determination of well locations using SOI Tool results in the well placements as shown by Table 8. Table 8 Well Placements Using SOI Tool Field X. A significant improvement in oil recovery when using SOI Tool has been shown by Table 9. The SOI proposed wells show a satisfying result with the highest Recovery Factor of 45.4%. Furthermore, SOI Tool results a recovery factor enhancement of 0.56% compared with previous study-b, 3.68% compared with previous study-a and 7.86% compared with the trial and error method. In other words, the utilization of SOI Tool is able to provide extra 157.4 MSTB oil compared with previous study-b, extra 1,036.2 MSTB oil compared with previous study-a, or extra 2,215.9 MSTB oil compared with the conventional trial and error method. Fig. 13 shows how extremely SOI Tool drains a huge part of oil in this case study reservoir. This increase of oil recovery will surely increase field revenue and lead to the escalation of economic benefits. (a) Table 9 Comparison of the Recovery of Trial and Error and Proposed Methods Field X. Method Cumulative Oil Production Recovery Factor (STB) (%) Trial and Error 10,579,501 37.5% Previous Study-A 11,759,142 41.7% Previous Study-B 12,638,051 44.8% SOI Tool 12,795,366 45.4% (b) Fig. 13 (a). Initial Oil Saturation of Field X. (b). Final Oil Saturation of Field X.

11 Wardana Saputra - 12211031 Case Study-2 (Green Gas Field: Field Y). As there is no previous studies conducted in this field, SOI Tool results will be compared only with conventional trial and error method. The determination of well locations for this Field Y using conventional trial and error method results in the well placements as shown by Table 10. Meanwhile, the determination of well locations using SOI Tool results in the well placements as shown by Table 11. Table 10 Well Placements Using Conventional Trial and Error Method Field Y. Well X Y Perforated Layers Well 1 46 26 11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30 Well 2 49 42 11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30 Well 3 52 30 11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30 Table 11 Well Placements Using SOI Tool Field Y. As same as case study-1, SOI proposed wells for case study-2 also shows a satisfying result compared with conventional trial-error method as shown by Table 12. By drilling only one well (1 st SOI Rank), the field can achieve a great recovery factor of 93.5% which is 9.8% or 12.63 BSCF better than conventional trial and error method. By adding number of wells, the recovery factor of both methods would be increasing due to the increase of plateau period as shown by Fig. 14. Table 12 Comparison of the Recovery of Trial and Error and SOI Tool Field Y. Conventional Trial-Error Method SOI Tool Number of Drilled Wells Cumulative Gas Production (BSCF) Recovery Factor (%) Number of Drilled Wells Cumulative Gas Production (BSCF) Recovery Factor (%) 1 Well 107.33 83.6% 1 Well 119.96 93.5% 2 Wells 118.41 92.3% 2 Wells 121.95 95.0% 3 Wells 120.50 93.9% 3 Wells 122.41 95.4% Fig. 14 Simulated Gas Production Rate of Field Y. As shown in Table 12, the increase of recovery factor from 2 wells to 3 wells is less than the increase of recovery factor from 1 wells to 2 wells. It could be happened because the adding of new wells has an optimum value. Inferential, by using SOI Tool, the optimum value of the field is only 2 wells which is less than the conventional trial-error results. By reducing the required number of wells, field investment costs could be reduced significantly. Indeed, this also leads the economic benefits. The gas saturation distribution of this Field Y before and after drilling new wells is shown by Fig. 15.

12 Wardana Saputra - 12211031 (a) (b) Fig. 15 (a). Initial Gas Saturation of Field Y. (b). Final Gas Saturation of Field Y. Case Study-3 (Brown Oil Field: Field Z). This case study is proposed to examine the competency of developed software in infill purposes of a brown oil field with 26 existing wells. The determination of well locations for this Field Z using conventional trial and error method results in the well placements as shown by Table 13. The determination of well locations by previous study and developed software is shown by Table 14 and Table 15. Table 13 Well Placements Using Trial-Error Method. Well X Y Perforated Layers Well 1 42 16 5,6,7,8,9,10,11,12,31,32,33,34 Well 2 100 17 5,6,7,8,9,10,11,12,31,32,33,34 Well 3 43 22 5,6,7,8,9,10,11,12,31,32,33,34 Well 4 71 10 5,6,7,8,9,10,11,12,31,32,33,34 Table 14 Well Placements Using Previous Study Field Z. Well X Y Perforated Layers Well 1 76 21 5,6,7,8,9,10,11,12,31,32,33,34 Well 2 97 19 5,6,7,8,9,10,11,12,31,32,33,34 Well 3 89 15 5,6,7,8,9,10,11,12,31,32,33,34 Well 4 79 13 5,6,7,8,9,10,11,12,31,32,33,34 Table 15 Well Placements Using SOI Tool Field Z. A significant improvement in oil recovery when using developed software has been shown by Table 14. The SOI infill wells show a gratifying result with the highest Recovery Factor of 50.24%. SOI infill wells result a recovery factor enhancement of 0.59% compared with previous study and 0.8% compared with the trial and error method. In other words, the utilization of SOI Tool is able to provide extra 996.3 MSTB oil compared with previous study and extra 1,347.1 MSTB oil compared with the conventional trial and error method. Fig. 16 shows the changes in saturation distribution of brown Oil Field Z before infill and after infill new wells. Table 14 Recovery Factor Before and After Infill. Scenario Cumulative Oil Production (STB) Recovery Factor (%) Base Case Prior to Infill 69,666,696 41.55% Base + 4 conventional wells 82,897,288 49.44% Base + 4 previous study wells 83,248,016 49.65% Base + 4 SOI Infill wells 84,244,352 50.24% The satisfying result of SOI Tool should boost field project developments even in the period of low oil prices. SOI Tool could maximize the profitability of field development projects with producing the highest field recovery.

13 Wardana Saputra - 12211031 (a) Fig. 16 (a). Prior to Infill Gas Saturation of Field Z. (b). Final Gas Saturation of Field Z. Sensitivity Analysis of SOI Values. A sensitivity analysis could be done to evaluate the SOI values of each rank. This evaluation uses the SOI results of Case Study-2 as shown in Table 11. Each rank has a corresponding SOI value which will be simulated to analyze how far the difference of SOI value affects the difference of recovery factor. It is important in the case when the drilling activity of the first rank location is not possible to do, forcefully the second rank location should be selected as the new infill candidates. Fig. 17 shows a chart describes SOI values of the three ranks. Rank-1 has SOI value of 1 which is the maximum value of normalized cumulative SOI. It shows that this location is the best among the others, so it has been chosen as the first rank. The second rank has SOI value of 0.98 and the third rank has SOI value of 0.94, lower rank depicts lower SOI value. (b) Fig. 17 Results Chart of Case Study-2. Table 17 shows the recovery factor of different three SOI well ranks. A graphical plot between SOI value and its corresponding recovery factor could be made as shown in Fig. 18. This plot clearly shows a strong relationship between SOI value and recovery factor as revealed by a straight trend-line of the points. It states that higher SOI value depicts higher recovery factor. Indeed the selection of these ranks by SOI Tool has just been validated to result only the best recovery factor. Table 17 Recovery Factor of Various Ranks. Well to be drilled Cumulative Gas Production (BSCF) Recovery Factor (%) SOI value rank-1 119.96 93.5% 1.00 rank-2 119.60 93.2% 0.9832 rank-3 118.62 92.4% 0.9403

14 Wardana Saputra - 12211031 Fig. 18 Relationship between SOI Values and Its Corresponding Recovery Factors. Back to answer the goals of this sensitivity analysis, Fig. 19 shows the simulated cumulative gas production of each rank. It looks like the differences among the recovery factor of these three ranks is not significant enough. So, it is allowed to substitute Rank-1 to Rank-2 so long as the SOI value difference is not significant enough. Conclusions Fig. 19 Simulated Cumulative Gas Production of Various Ranks. A Simulation Opportunity Index based software application SOI Tool has been developed to optimize the selection of vertical well locations and has demonstrated to be an advance searching method that is able to be implemented in real oil and gas field development with less time and effort. SOI Tool offers better results and high recovery for a green oil field compared with the conventional trial-error method by approximately 7.86% or 2.22 MMSTB, previous study-a by 3.68% or 1.04 MMSTB, and previous study-b by 0.56% or 157 MSTB. For green gas field, while one well of conventional trial-error method only achieves 83.6% recovery factor, one SOI well can achieves up to 93.5% which equals to an extra gas production of 12.63 BSCF. In this gas field, SOI Tool helps reducing the required number of wells to 2 wells only with the optimum recovery factor of 95%. SOI Tool also offers better results in infill purposes of a mature oil field. The SOI infill wells among 26 existing wells result a recovery factor enhancement of 0.59% or 996.3 MSTB oil compared with previous study and 0.8% or 1,3471 MSTB oil compared with the trial and error method. Sensitivity analysis which was already done shows a strong relationship between SOI value and recovery factor. It validates that the

15 Wardana Saputra - 12211031 ranks which are selected by SOI Tool with high SOI value would depict only higher recovery factor. The satisfying results of SOI Tool should boost field project developments even in the period of low oil prices. On one side, SOI Tool could save the budgets by minimizing the required number of wells. On the other hand, SOI Tool could maximize profit with producing the highest recovery. Future Developments SOI Tool is developed to optimize the selection of vertical well locations, since vertical wells are more economical than horizontal wells. Besides that, algorithm development for vertical wells is relatively easier than that of horizontal wells. Indeed the future algorithm development for horizontal wells should more complicated to be done. Furthermore, since the developed software only considers the technical aspects in determining the optimum well placements, the consideration of economic aspects would be required for the advancement of the developed software. Acknowledgments The author is greatly indebted to Prof. Dr. Tutuka Ariaji, M.Sc., Ph.D., one of the distinguished lecturers in the Department of Petroleum Engineering, Bandung Institut Technology, for his counsel and guidance during final project process and SOI Tool development.

16 Wardana Saputra - 12211031 Nomenclature S o = Current oil saturation at end of History Matching (HM) period or prior to infill, fraction S or = Residual oil saturation, fraction S g = Current gas saturation at end of History Matching (HM) period or prior to infill, fraction S gr = Residual gas saturation, fraction ϕ = Porosity, fraction k = Permeability, md h = Height of net sand or (DZxNTG) in simulation models, ft µ o = Oil viscosity at end of HM period or prior to infill, cp µ g = Gas viscosity at any pressure calculated by Carr-Kobayashi-Burrow correlation, cp Z = Gas compressibility factor at any pressure calculated by Beggs-Brill correlation, fraction P = Current pressure at end of HM period or prior to infill, psi P abn = Abandonment pressure, psi ψ = Pseudo-pressure at current pressure (P), psi 2 /cp ψ abn = Pseudo-pressure at abandonment pressure (P abn), psi 2 /cp References A. Molina, A. Rincon, Repsol YPF., 2009. Exploitation Plan Design Based on Opportunity Index Analysis in Numerical Simulation Models. SPE 122915. M. Ghazali, M. Razib, Saudi Aramco, 2011. Optimizing Development Strategy and Maximizing Field Economic Recovery Through Simulation Opportunity Index. SPE 148103. B. Chuah, S., et alet al, Petronas, 2014. Reservoir Engineering Aspect of Well Construction for Cost Effective Field Development: Advances in Drainage Point Selection. IPTC-18243-MS. I. T. Rau, T. Ariadji, 2013. A New Software Development of Genetic Algorithm Application to Optimize Vertical Well Placements Based on Basic Reservoir Rock Properties Considering Existing Wells, Drainage Radius and Fault Existence for Multi-Layer Reservoirs: Final Project at the Department of Petroleum Engineering, Institut Teknologi Bandung. M. L. Putra, 2010. The Role of Genetic Algorithm for Well Placement Optimization in the X Oil Field: Final Project at the Department of Petroleum Engineering, Institut Teknologi Bandung.

17 Wardana Saputra - 12211031 Appendix A Case Study-1: Green Oil Field Fig. A-1 Field Data - Oil Field X. Fig. A-2 Grid Data - Oil Field X.

18 Wardana Saputra - 12211031 Fig. A-3 Existing Wells - Oil Field X. Fig. A-4 Drainage Plan - Oil Field X.

19 Wardana Saputra - 12211031 Fig. A-5 Results - Oil Field X. Fig. A-6 Results Chart - Oil Field X.

20 Wardana Saputra - 12211031 Fig. A-7 Results Map - Oil Field X.

21 Wardana Saputra - 12211031 Appendix B Case Study-2: Green Gas Field Fig. B-1 Field Data - Gas Field Y. Fig. B-2 Grid Data - Gas Field Y.

22 Wardana Saputra - 12211031 Fig. B-3 Existing Wells - Gas Field Y. Fig. B-4 Drainage Plan - Gas Field Y.

23 Wardana Saputra - 12211031 Fig. B-5 Results - Gas Field Y. Fig. B-6 Results Chart - Gas Field Y.

24 Wardana Saputra - 12211031 Fig. B-7 Results Map - Gas Field Y.

25 Wardana Saputra - 12211031 Appendix C Case Study-3: Brown Oil Field Fig. C-1 Field Data - Oil Field Z. Fig. C-2 Grid Data - Oil Field Z.

26 Wardana Saputra - 12211031 Fig. C-3 Existing Wells - Oil Field Z. Fig. C-4 Drainage Plan - Oil Field Z.

27 Wardana Saputra - 12211031 Fig. C-5 Results - Oil Field Z. Fig. C-6 Results Chart - Oil Field Z.

28 Wardana Saputra - 12211031 Fig. C-7 Results Map - Oil Field Z