Determining the optimal location of the wells is a crucial decision to be made during a field development plan. A reservoir simulator called "SimBest II" has been used in the present study. The oil field under study is a sector of South Rumaila oil field/main pay. Two methods of optimization have been adopted in the present study. The first one is manual optimization and the second method is automatic optimization. Genetic Algorithm (GA) was used as the automatic optimization method to find the best number and locations of the wells. Genetic algorithm depends on the principle of artificial intelligence similar to Darwin's theory of Natural Selection. The genetic program is coupled with the simulator in order to re-evaluate the optimized wells at each iteration. The genetic algorithm computer program gave results similar to the results that are obtained by the manual method with less computer time. Both methods have been adopted according to the aspects of net present value (economic evaluation) as objective function. According to the relationship between net present value and future production time, the abandonment time was estimated before end of December 2014 for all proposed future scenarios. The optimal future scenario was with water injection of 15000 surface bbls/day per well. The optimal number of additional wells for this case was nineteen wells, but it may be impossible to drill such a number of wells in the one year especially in Iraq. Moreover, that an alternative option of drilling three wells with reduction of 0.07399 % in NPV has been considered. This option is also suggested if the surface injection facilities cannot handle the injection of 15000 surface bbls/day. The optimal number of additional wells for this choice is also three wells.
A practical optimization approach has been coupled with a commercial reservoir simulator to evaluate the optimal number and locations of infill wells in a conceptual sector of the Main Pay in South Rumaila Oil Field in Iraq. This field, of 58-years of production, has 40 producing wells. There is also an edge water drive from the infinite active aquifer at the east and west flanks. The water drive from the west flank is more than 20 times the drive from the east flank. Therefore, twenty injection wells have been drilled located at the east flank to maintain and balance the reservoir pressure. After achieving the reliable history matching and running the simulator for a future period until 31 December 2020, the optimization approach has been implemented via spreadsheet considering the aspects of Net Present Value as objective function. The Net Present Value (NPV) is a function of economic variables representing costs and revenues. The revenue is directly proportional to the cumulative oil production that has been predicted by the reservoir model for the entire field. The costs include capital (CAPEX) and operational (OPEX) expenditures. There are ten assigned location of wells taken as a premise to be production wells. These 10 locations have been ranked according to the highest value of permeability and oil saturation spatial distribution in the reservoir in order to be evaluated considering achieving the highest NPV. The optimal number of infill wells was three, six, and ten when the NPV is calculated by the end of 2015, 2017, and 2022, respectively and all the wells at the three cases located at the crest of the reservoir. These optimal wells don't have the maximum cumulative oil production; however, they have the maximum Net Present Value especially at the first two prediction cases.
An Interdisciplinary study for increasing oil recovery has been made in the present paper. This work has been adopted in the Upper Sandstone member/Zubair formation in South Rumaila Oil Field. The work was achieved by using optimization techniques for determining the optimal future reservoir performance regarding to infill drilling. Adaptive Genetic Algorithm (AGA) has been adopted in this paper to optimize the count and locations of infill wells. AGA uses Fuzzy Logic (FL) to determining optimal crossover rate, mutation rate and crossover form for each generation; in order to find accurate prediction. The main parameters depended in this study is the cumulative oil production obtained from the output of reservoir simulation software. This optimization tool depended on using the objective function of Net Present Value (NPV) as economic analysis. The optimal number of infill wells is three wells that have maximum cumulative oil production and maximum value of NPV. The locations of these optimal infill wells located in the crest of the oil field and far away from the east and west flanks because of the strong water drive from the infinite acting aquifer.
Planning of an optimal field development scenario in a mature oil field is a crucial decision since it requires an efficient design of integrated reservoir modeling. One of the most imperative field development scenario is determining the locations of infill wells. The reservoir under study is the main pay in the South Rumaila oil field. This is a mature oil field located in the South of Iraq with around 58 years of production. It has 40 producing wells and 20 injection wells. An efficient reservoir simulation model has been coupled in sequence with a stable and well-conditioned genetic algorithm to optimize the infill well locations. A Genetic Algorithm offers an efficient search method that can be used as a powerful optimization tool by randomly generating potential solutions in order to achieve increasingly better results by applying a set of operators:Selection, Crossover (Recombination), and Mutation.In this study, a black oil reservoir model has been used to evaluate the reservoir and predict its future performance. After attaining a considerable history matching, the simulator has been coupled with an Adaptive Genetic Algorithm (AGA). This algorithm is coupled with the simulator in order to re-evaluate the optimized wells at each iteration. Net Present Value (NPV) has been adopted here as an objective function. The initial population consists of a random number of chromosomes that have eight genes that represent the number of possible new proposed wells that have the highest values of permeability and oil saturation at the last time step. The genetic algorithm takes two parents randomly from the population and produces two children (offspring) by applying the operators such as crossover, mutation, and replacement on the two parents.Subsequently, the program sorts the population' chromosomes from best to worst. The resulting chromosome represents the optimal wells at this iteration. Then the AGA program re-inputs the optimized wells in the input file of the simulator to repeat the processes for many iterations until the optimal solution with the highest NPV is obtained. The entire procedure has led to optimize three infill wells that have the highest NPV among the other solutions.
Thermal oil recovery methods have been widely used not only in heavy oil reservoirs, but also in light oil reservoir with Waterflooding to improve oil recovery. The Steamflooding could be considered as an effective way to enhance the oil displacement especially in heterogeneous reservoirs The field, of 58-years of production history, is located in South of Iraq. It has 40 producing wells. There was an infinite active aquifer located at the east and west flanks. The strength of this aquifer from the west flank is much larger than its in the east flank because the reservoir permeability at the eastern boundaries is lower than as at the western one for all the layers; therefore, Twenty injection wells were drilled at the east flank to maintain the aquifer water approaching to the reservoir. The average surface area for this reservoir is 142 km2 and average formation depth of 10350 ft subsea with a maximum vertical oil column of 350 ft. Average porosity is 21%. The oil is 34°API with an average initial bubble point pressure of 2660 psia. Current reservoir pressure is approximately 4200 psia and the reservoir temperature is 210°F. In this study, a thermodynamic reservoir simulation has been adopted to investigate the competence of Steamflooding to improve oil recovery. The objective of this work was to examine the feasibility of steam-injection processes, so a thermodynamical reservoir model (CMG-STARS) has been applied to demonstrate the effect of using steam injection as a heating agent to increase the sweep efficiency in this heterogeneous formation. The twenty injection wells have been converted to steam injection for twelve future prediction years. The process has demonstrated a considerable increase of the cumulative oil production. This result has been compared with the base scenario of water injection at the same injection rates of 10,000 STB/DAY per well. The water injection scenario has been done by CMG-IMEX. This incremental has been proved over most of the production wells that have distributed among the reservoir by showing a significant difference between the two cases.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.