Generative Well Pattern Design Applied to a Giant Mature Field Leads to the Identification of Major Drilling Expenditure Reduction Opportunity

Lepphaille, Maddalen; Thenon, Arthur; Bergey, P.; Salley, Baptiste; Sadok, Abdelaziz Ben; Koeck, C.

doi:10.2118/203152-ms

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…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%

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: Introductionmentioning

confidence: 99%

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

2021

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Application of Machine Learning in a Giant Mature Reservoir to Speed-Up Infill Prospects Screening, Optimize Field Development and Improve the Ultimate Recovery Factor

Fabbri

Reddicharla

Shi

et al. 2023

Day 2 Wed, January 25, 2023

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In giant reservoirs, production sustainability strongly depends on the identification of opportunities for infill drilling. This paper presents the use of Machine Learning to speed-up and improve the efficiency of the evaluation of future infill wells, in an effort to optimize field development of a Giant Mature reservoir Onshore Abu Dhabi. In the mature giant carbonate reservoir studied, more than 420 wells are already drilled with consistent spacing but with varying orientations. This paper illustrates some examples of settings that are difficult to assess without geometric calculations, leading to time-consuming opportunity identification and classification. The minimum set of input for the program includes existing wells trajectories, faults polygons, contact, and production data. Users can define the minimum drainage area for each well, maturity criteria and drain length. For each subsurface target identified, a polygon and simulation input are generated. The Python program is developed and run on an in-house platform and solve the future wells positioning in three main steps: (1) Geometric screening and identification of locations with required spacing, (2) Analysis of nearby well performance, (3) automatic generation of simulation input for evaluation of the subsurface target.

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Generative Well Pattern Design Applied to a Giant Mature Field Leads to the Identification of Major Drilling Expenditure Reduction Opportunity

Cited by 2 publications

References 3 publications

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

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

Application of Machine Learning in a Giant Mature Reservoir to Speed-Up Infill Prospects Screening, Optimize Field Development and Improve the Ultimate Recovery Factor

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