Robust integrated optimization of well placement and control under field production constraints

Salehian, Mohammad; Sefat, Morteza Haghighat; Muradov, Khafiz

doi:10.1016/j.petrol.2021.108926

Cited by 15 publications

(4 citation statements)

References 56 publications

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“…Assuming 𝑛 𝑐 clusters, the optimum number of clusters (𝑛 𝑐 𝑜𝑝𝑡 ) is determined by comparing the average silhouette value (𝑆𝑖𝑙 ̅̅̅̅ (𝑛 𝑐 )) for different numbers of clusters (𝑛 𝑐 ), where the maximum silhouette value indicates the best quality of clustering. Detailed information about the standard procedure of calculating average silhouette value can be found in Salehian, Sefat [12] and Haghighat Sefat, Elsheikh [37].…”

Section: Reservoir Model Realization Selectionmentioning

confidence: 99%

“…Single-level optimization frameworks have been developed to optimize only one type of control variables, such as well locations [1][2][3][4][5][6][7] or well control settings like flow rate or pressure [8][9][10][11][12][13][14][15][16][17][18]. These methods may not be appropriate where the optimization problem involves multiple levels, as they do not capture the potential correlations or interference among control variables at different levels.…”

Section: Introductionmentioning

confidence: 99%

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A robust, multi-solution framework for well placement and control optimization

2021

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Field development and control optimization aim to maximize the economic profit of oil and gas production while considering several sources of uncertainty. This results in a high-dimensional optimization problem with a computationally demanding and uncertain objective function based on the simulated reservoir model. The limitations of many current robust optimization methods are: 1) it is single-level optimization (e.g. optimization of well locations/placement only; or of well production/injection control variables only) that ignores interference between the control variables from different levels; and 2) they provide a single optimal solution, whereas operational problems often add unexpected constraints likely to reduce that optimal, inflexible solution to a sub-optimal scenario. This paper presents a robust, multi-solution framework based on sequential iterative optimization of control variables at multiple levels using the Simultaneous Perturbation Stochastic Approximation (SPSA) optimization algorithm. A systematic realization selection process, tailored to the objective of the subsequent optimization stage, is used to select a small representative ensemble of reservoir model realizations to be used for calculating the expected objective value. The estimated gradients are calculated using a 1:1 ratio mapping ensemble of control variables perturbations at each iteration onto the ensemble of selected reservoir model realizations to reduce the computational cost. An ensemble of close-to-optimum solutions is then chosen from each level (e.g. from the well placement optimization level) and transferred to the next level of optimization (e.g. where the control settings are optimized), and this loop continues until no significant improvement is observed in the expected objective value. Fit-for-purpose clustering techniques are developed to systematically select an ensemble of solutions, with maximum differences in control variables but close-to-optimum objective values, at each optimization level.The proposed framework has been tested on a benchmark case study (Brugge field). Multiple solutions are obtained with different well locations and control settings but close-to-optimum objective values. We show that suboptimal solutions from an early optimization level can approach and even outdo the optimal one at the next level(s). Results demonstrate the advantage of the developed framework in more efficient exploration of the search space and providing the much-needed operational flexibility to field operators.

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Section: Reservoir Model Realization Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A robust, multi-solution framework for well placement and control optimization

2021

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“…However, for China, which lacks oil and gas resources, planned drilling to meet the country's demand for oil and gas resources takes precedence over achieving economic benefits. One of the pivotal steps of the field plan is to finalize the drilling schedule [ 5 ], determining the timing and the relevant number of drillings—specifically, how many wells should be drilled in a given month [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

An optimization model for monthly time-step drilling schedule under planned field production

Ouyang,

Geng,

Zhai

2024

Heliyon

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“…To date, researchers have developed injection-production optimization models using different algorithms. − Isebor and Durlofsky developed the particle swarm optimization–mesh adaptive direct search (PSO–MADS) hybrid optimization algorithm to determine optimal development and production plans considering the objective of maximizing oil recovery while minimizing water injection . Ahmadloo et al develop a new diagnostic model with multivariate partial least squares (PLS), response surface methodology (RSM), and artificial neural network (ANN) to improve the quality of predictions .…”

Section: Introductionmentioning

confidence: 99%

Optimization Method of Three-Dimensional Equilibrium Displacement in Thin Interbed Reservoirs

Jiang,

Cheng,

et al. 2023

ACS Omega

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Most thin interbed reservoirs face a common problem that a nonequilibrium injection and production relationship in plane and vertical directions results in quick water breakthrough, rapid water-cut rise, and a poor water flooding efficiency in a single layer. A finer injection-production strategy should be developed to avoid serious water channeling and an ineffective water cycle. To narrow this gap, this work presents a three-dimensional intelligent equilibrium displacement model (3D-IEDM) to optimize water flooding in thin interbed reservoirs. A water-injection splitting model is first established to determine the water-injection rate of each layer based on displacement pressure and flow resistance. Then, water saturation is calculated for the injection-production well group based on the material balance principle. To achieve three-dimensional equilibrium flooding, the minimum water saturation variance is chosen as the optimization target and the improved particle swarm optimization algorithm is employed to reduce the optimization time caused by iterative calculations. Finally, the 3D-IEDM is programmed as software to provide a quantitative equilibrium flooding optimization scheme in an actual oilfield. The implementation in the pilot B36 well group test of the PL oilfield indicates that the optimization velocity of the 3D-IEDM can optimize the vertical water injection profile of thin interbed reservoirs and improve the sweep efficiency, and the length of time is approximately 14 times less than that of conventional simulator-based methods. Compared with the conventional injection-production scheme, the initial productivity of the pilot well group using the 3D-IEDM increases by 6.45%, and the utilization factor of water injection improves by 15%.

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Robust integrated optimization of well placement and control under field production constraints

Cited by 15 publications

References 56 publications

A robust, multi-solution framework for well placement and control optimization

A robust, multi-solution framework for well placement and control optimization

An optimization model for monthly time-step drilling schedule under planned field production

Optimization Method of Three-Dimensional Equilibrium Displacement in Thin Interbed Reservoirs

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