Fluid Injection Optimization Using Modified Global Dynamic Harmony Search

Abstract One of the mostly used enhanced oil recovery methods is the injection of water or gas under pressure to maintain or reverse the declining pressure in a reservoir. Several parameters should be optimized in a fluid injection process. The usual optimizing methods evaluate several scenarios to find the best solution. Since it is required to run the reservoir simulator hundreds of times, the process is very time consuming and cumbersome. In this study a new intelligent method of optimization, called "global dynamic harmony search" is used with some modifications in combination with a commercial reservoir simulator (ECLIPSE ® ) to determine the optimum solution for fluid injection problem unknowns. Net present value (NPV) is used as objective function to be maximized. First a simple homogeneous reservoir model is used for validating the developed method and then the new optimization method is applied to a real model of one of the Iran oil reservoirs. Three strategies, including gas injection, water injection, and well placement are considered. Comparing the values of NPV and field oil efficiency (FOE) of gas injection and water injection strategies, it is concluded that water injection strategy surpasses its rival. Considering water injection to be the base case, a well placement optimization is also done and best locations for water injection wells are proposed. The results show the satisfying performance of the algorithm regarding its low iterations

Multi-solution well placement optimization using ensemble learning of surrogate models

Well location optimization aims to maximize the economic profit of oil and gas field development while respecting various constraints. The limitations of the currently available well placement optimization workflows are their 1) high computational requirements, which makes them inappropriate for full-field applications where a large number of wells have to be optimized using a computationally expensive simulation model; and 2) providing a single optimal solution, whereas on-site operational problems often add unforeseen constraints that result in adjustments to this optimal, inflexible scenario degrading its value. This study presents a multi-solution, surrogate models (SMs)-assisted optimization framework to deliver diverse, close-to-optimum well placement scenarios at a reasonable computational cost. Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm is used as the optimizer while diversity in optimal solutions is achieved by multiple, parallel runs of the optimizer with different starting points. Convolutional Neural Network (CNN) is used as the SM, to partly substitute the computationally expensive reservoir model runs during the optimization process. A new, adjusted Latin Hypercube Sampling (aLHS) procedure is developed to generate initial training datasets with diverse well placement scenarios while respecting reservoir boundaries and well spacing constraints. An ensemble of CNNs is pre-trained using the generated dataset to enhance the robustness of the surrogate modeling as well as to allow estimation of the SM's prediction quality for new data points. The ensemble of CNNs is adaptively updated during the optimization process using selected new data points, to improve the SM's prediction accuracy. To the best of our knowledge, this is the first application of ensemble learning strategy to a well placement optimization problem. The added value of the framework is demonstrated by comparing three optimization approaches on the Brugge and Egg field benchmark case studies. The approaches are 1) ‘no SM’: using the actual reservoir model only, 2) ‘Offline SM’: the optimization is performed using SM-only that is pre-trained using initial training datasets generated by the actual reservoir model, and 3) ‘Online SM’: pre-trained CNNs are adaptively updated during the optimization process using new datasets generated using the actual reservoir model. The surrogate-assisted optimization approach substantially reduced the computation time, while a greater objective value was achieved by employing the adaptive learning strategy due to the enhanced prediction accuracy of the SMs. Multiple diverse solutions were obtained with different well locations but close-to-optimum objective values, which allows a more efficient exploration of the search space at a significantly reduced computational cost. The presented workflow integrates critical challenges that are correlated, yet often addressed independently, providing the much-required operational flexibility and computational efficiency to field operators when selecting from the optimal well placement scenarios

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

Optimal field development and control aim to maximize the economic profit of oil and gas production while considering various constraints. This results in a high-dimensional optimization problem with a computationally demanding and uncertain objective function based on the simulated reservoir models. The limitations of many current robust optimization methods are: 1) they optimize only a single level of control variables (e.g. well locations only; or well production/injection scheduling only) that ignores the interferences between control variables from different levels; and 2) they provide a single optimal solution, whereas operational problems often add unexpected constraints that result in adjustments to this optimal solution scenario degrading its value. This paper presents a robust, multi-solution framework based on sequential iterative optimization of control variables at multiple levels. Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm is used as the optimizer while 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. An ensemble of closeto- 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 realizations to capture the underlying model uncertainties, as well as an ensemble of solutions with sufficient differences in control variables but close-to-optimum objective values, at each optimization level. The proposed framework has been tested on the Brugge benchmark field case study. Multiple solutions are obtained with different well locations and control settings but close-to-optimum objective values, providing the much-needed operational flexibility to field operators. We also show that suboptimal solutions from an early optimization level can approach and even outdo the optimal one at the next level(s) demonstrating the advantage of the developed framework in a more efficient exploration of the search space

Development of a Multi-Solution Framework for Simultaneous Well Placement, Completion, and Control Optimization

Optimal field development and control aim to maximize the economic profit of oil and gas production. This, however, results in a complex optimization problem with a large number of correlated control variables at different levels (e.g. well locations, completions and controls) and a computationally expensive objective function (i.e. a simulated reservoir model). The typical limitations of the existing optimization frameworks are: (1) single-level optimization at a time (i.e. ignoring correlations among control variables at different levels); and (2) providing a single solution only whereas operational problems often add unexpected constraints likely to reduce the -optimal-, inflexible solution to a sub-optimal scenario. The developed framework in this paper is based on sequential iterative optimization of control variables at different levels. An ensemble of close-To-optimum solutions is selected from each level (e.g. for well location) and transferred to the next level of optimization (e.g. To control settings), and this loop continues until no significant improvement is observed in the 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 level of optimization. The framework also considers pre-defined constraints such as the minimum well spacing, irregular reservoir boundaries, and production/injection rate limits. The proposed framework has been tested on a benchmark case study, known as the Brugge field, to find the optimal well placement and control in two development scenarios: with conventional (surface control only) and intelligent wells (with additional zonal control using Interval Control Valves). Multiple solutions are obtained in both development scenarios, with different well locations and control settings but close-To-optimum objective values. We also show that suboptimal solutions from an early optimization level can approach and even outdo the optimal one at the higher-level optimization, highlighting the value of the here-developed multi-solution framework in exploring the search space as compared to the traditional single-solution approaches. The development scenario with intelligent completion installed at the optimal well location and optimally controlled during the production period achieved the maximum added value. Our results demonstrate the advantage of the developed multi-solution optimization framework in providing the much-needed operational flexibility to field operators