Jamal Ahdeema | Heriot-Watt University (original) (raw)
Papers by Jamal Ahdeema
Energies , 2023
Multilateral wells (MLWs) equipped with multiple flow control devices (FCDs) are becoming increas... more Multilateral wells (MLWs) equipped with multiple flow control devices (FCDs) are becoming increasingly favored within the oil sector due to their ability to enhance well-to-reservoir exposure and effectively handle unwanted fluid breakthrough. However, combining various types of FCDs in multilateral wells poses a complex optimization problem with a large number of highly correlated control variables and a computationally expensive objective function. Consequently, standard optimization algorithms, including metaheuristic and gradient-based approaches, may struggle to identify an optimal solution within a limited computational resource. This paper introduces a novel hybrid optimization (HO) framework combining particle swarm optimization (PSO) and Simultaneous Perturbation Stochastic Approximation (SPSA). It is developed to efficiently optimize the completion design of MLWs with various FCDs while overcoming the individual limitations of each optimization algorithm. The proposed framework is further enhanced by employing surrogate modelling and global sensitivity analysis to identify critical parameters (i.e., highly sensitive) that greatly affect the objective function. This allows for a focused optimization effort on these key parameters, ultimately enhancing global optimization performance. The performance of the novel optimization framework is evaluated using the Olympus benchmark reservoir model. The model is developed by three intelligent dual-lateral wells, with inflow control devices (ICDs) installed within the laterals and interval control valves (ICVs) positioned at the lateral junctions. The results show that the proposed hybrid optimization framework outperforms all industry-standard optimization techniques, achieving a Net Present Value of approximately USD 1.94 billion within a limited simulation budget of 2500 simulation runs. This represents a substantial 26% NPV improvement compared to the open-hole case (USD 1.54 billion NPV). This improvement is attributed to more efficient water breakthrough management, leading to a notable 24% reduction in cumulative water production and, consequently, a 26% increase in cumulative oil production.
Energies, 2023
Multilateral wells (MLWs) equipped with multiple flow control devices (FCDs) are becoming increas... more Multilateral wells (MLWs) equipped with multiple flow control devices (FCDs) are becoming increasingly favored within the oil sector due to their ability to enhance well-to-reservoir exposure and effectively handle unwanted fluid breakthrough. However, combining various types of FCDs in multilateral wells poses a complex optimization problem with a large number of highly correlated control variables and a computationally expensive objective function. Consequently, standard optimization algorithms, including metaheuristic and gradient-based approaches, may struggle to identify an optimal solution within a limited computational resource. This paper introduces a novel hybrid optimization (HO) framework combining particle swarm optimization (PSO) and Simultaneous Perturbation Stochastic Approximation (SPSA). It is developed to efficiently optimize the completion design of MLWs with various FCDs while overcoming the individual limitations of each optimization algorithm. The proposed framework is further enhanced by employing surrogate modelling and global sensitivity analysis to identify critical parameters (i.e., highly sensitive) that greatly affect the objective function. This allows for a focused optimization effort on these key parameters, ultimately enhancing global optimization performance. The performance of the novel optimization framework is evaluated using the Olympus benchmark reservoir model. The model is developed by three intelligent dual-lateral wells, with inflow control devices (ICDs) installed within the laterals and interval control valves (ICVs) positioned at the lateral junctions. The results show that the proposed hybrid optimization framework outperforms all industry-standard optimization techniques, achieving a Net Present Value of approximately USD 1.94 billion within a limited simulation budget of 2500 simulation runs. This represents a substantial 26% NPV improvement compared to the open-hole case (USD 1.54 billion NPV). This improvement is attributed to more efficient water breakthrough management, leading to a notable 24% reduction in cumulative water production and, consequently, a 26% increase in cumulative oil production.
Society of Petroleum Engineers (SPE), 2023
Designing a well completion for multilateral wells with multiple types of flow control devices (F... more Designing a well completion for multilateral wells with multiple types of flow control devices (FCDs) can be a challenging optimization task due to a large number of correlated control variables and computationally demanding objective functions. Consequently, standard optimization workflows may fail to find the optimal design. The lack of a reliable optimisation workflow has forced the industry to adopt a simplified, snapshot approach to intelligent completion design, ignoring long-term dynamic reservoir performance.
In this work, a multistage optimization workflow named hybrid optimization (HO), has been developed for effectively optimizing the completion design of multilateral wells that are equipped with multiple types of FCDs. Differential evolution (DE), a metaheuristic optimisation algorithm, is utilized for initial exploration of the search space to identify promising regions, while the generated data are employed to develop a fast surrogate model to mimic the dynamic performance of the computationally expensive reservoir model. Global sensitivity analysis using the Sobol method is then performed with the aid of the developed and tested surrogate model, to divide control parameters into high and low impact groups. The Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm is employed at the final optimisation stage to perform a refined search in the optimal areas previously identified. The proposed framework offers engineers a set of guidelines to adjust the completion design, by modifying the most critical design parameters, in order to maximize production performance while minimizing installation and operational risks.
The new workflow has been tested on a 3-D, synthetic, representative reservoir model developed by an intelligent dual-lateral well equipped with inflow control devices (ICDs) inside the laterals, and interval control valves (ICVs) at the laterals’ junctions. The developed HO technique showed superior performance as compared to the current, standard optimization options relying on a single algorithm. It allows efficient dynamic optimization and delivers reliable results in a reasonable time, to replace the snap-shot designs which can be sub-optimal due to their dependency on a single timestep.
Energies , 2023
Multilateral wells (MLWs) equipped with multiple flow control devices (FCDs) are becoming increas... more Multilateral wells (MLWs) equipped with multiple flow control devices (FCDs) are becoming increasingly favored within the oil sector due to their ability to enhance well-to-reservoir exposure and effectively handle unwanted fluid breakthrough. However, combining various types of FCDs in multilateral wells poses a complex optimization problem with a large number of highly correlated control variables and a computationally expensive objective function. Consequently, standard optimization algorithms, including metaheuristic and gradient-based approaches, may struggle to identify an optimal solution within a limited computational resource. This paper introduces a novel hybrid optimization (HO) framework combining particle swarm optimization (PSO) and Simultaneous Perturbation Stochastic Approximation (SPSA). It is developed to efficiently optimize the completion design of MLWs with various FCDs while overcoming the individual limitations of each optimization algorithm. The proposed framework is further enhanced by employing surrogate modelling and global sensitivity analysis to identify critical parameters (i.e., highly sensitive) that greatly affect the objective function. This allows for a focused optimization effort on these key parameters, ultimately enhancing global optimization performance. The performance of the novel optimization framework is evaluated using the Olympus benchmark reservoir model. The model is developed by three intelligent dual-lateral wells, with inflow control devices (ICDs) installed within the laterals and interval control valves (ICVs) positioned at the lateral junctions. The results show that the proposed hybrid optimization framework outperforms all industry-standard optimization techniques, achieving a Net Present Value of approximately USD 1.94 billion within a limited simulation budget of 2500 simulation runs. This represents a substantial 26% NPV improvement compared to the open-hole case (USD 1.54 billion NPV). This improvement is attributed to more efficient water breakthrough management, leading to a notable 24% reduction in cumulative water production and, consequently, a 26% increase in cumulative oil production.
Energies, 2023
Multilateral wells (MLWs) equipped with multiple flow control devices (FCDs) are becoming increas... more Multilateral wells (MLWs) equipped with multiple flow control devices (FCDs) are becoming increasingly favored within the oil sector due to their ability to enhance well-to-reservoir exposure and effectively handle unwanted fluid breakthrough. However, combining various types of FCDs in multilateral wells poses a complex optimization problem with a large number of highly correlated control variables and a computationally expensive objective function. Consequently, standard optimization algorithms, including metaheuristic and gradient-based approaches, may struggle to identify an optimal solution within a limited computational resource. This paper introduces a novel hybrid optimization (HO) framework combining particle swarm optimization (PSO) and Simultaneous Perturbation Stochastic Approximation (SPSA). It is developed to efficiently optimize the completion design of MLWs with various FCDs while overcoming the individual limitations of each optimization algorithm. The proposed framework is further enhanced by employing surrogate modelling and global sensitivity analysis to identify critical parameters (i.e., highly sensitive) that greatly affect the objective function. This allows for a focused optimization effort on these key parameters, ultimately enhancing global optimization performance. The performance of the novel optimization framework is evaluated using the Olympus benchmark reservoir model. The model is developed by three intelligent dual-lateral wells, with inflow control devices (ICDs) installed within the laterals and interval control valves (ICVs) positioned at the lateral junctions. The results show that the proposed hybrid optimization framework outperforms all industry-standard optimization techniques, achieving a Net Present Value of approximately USD 1.94 billion within a limited simulation budget of 2500 simulation runs. This represents a substantial 26% NPV improvement compared to the open-hole case (USD 1.54 billion NPV). This improvement is attributed to more efficient water breakthrough management, leading to a notable 24% reduction in cumulative water production and, consequently, a 26% increase in cumulative oil production.
Society of Petroleum Engineers (SPE), 2023
Designing a well completion for multilateral wells with multiple types of flow control devices (F... more Designing a well completion for multilateral wells with multiple types of flow control devices (FCDs) can be a challenging optimization task due to a large number of correlated control variables and computationally demanding objective functions. Consequently, standard optimization workflows may fail to find the optimal design. The lack of a reliable optimisation workflow has forced the industry to adopt a simplified, snapshot approach to intelligent completion design, ignoring long-term dynamic reservoir performance.
In this work, a multistage optimization workflow named hybrid optimization (HO), has been developed for effectively optimizing the completion design of multilateral wells that are equipped with multiple types of FCDs. Differential evolution (DE), a metaheuristic optimisation algorithm, is utilized for initial exploration of the search space to identify promising regions, while the generated data are employed to develop a fast surrogate model to mimic the dynamic performance of the computationally expensive reservoir model. Global sensitivity analysis using the Sobol method is then performed with the aid of the developed and tested surrogate model, to divide control parameters into high and low impact groups. The Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm is employed at the final optimisation stage to perform a refined search in the optimal areas previously identified. The proposed framework offers engineers a set of guidelines to adjust the completion design, by modifying the most critical design parameters, in order to maximize production performance while minimizing installation and operational risks.
The new workflow has been tested on a 3-D, synthetic, representative reservoir model developed by an intelligent dual-lateral well equipped with inflow control devices (ICDs) inside the laterals, and interval control valves (ICVs) at the laterals’ junctions. The developed HO technique showed superior performance as compared to the current, standard optimization options relying on a single algorithm. It allows efficient dynamic optimization and delivers reliable results in a reasonable time, to replace the snap-shot designs which can be sub-optimal due to their dependency on a single timestep.