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A Comprehensive Review of Advancements in AI-Based Techniques for field development optimization

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年第7期50卷 5279-5301页

作者： Al-Ismael, Menhal A. Jamal, Mohammad S. Awotunde, Abeeb A. Saudi Aramco Dhahran Saudi Arabia King Fahd Univ Petr & Minerals Dept Petr Engn Dhahran Saudi Arabia King Fahd Univ Petr & Minerals CIPR Dhahran Saudi Arabia

Optimizing well placement and production strategy in hydrocarbon reservoirs is a critical task during field development planning. Various optimization algorithms have been proposed in the literature to optimize different field development problems. Recent research in this area has shifted toward using artificial intelligence (AI) to assist field development optimization in an attempt to establish approaches that are more effective. This paper presents a comprehensive review of recent research on AI-based optimization techniques applied to field development, focusing on studies published in the last ten years. We identified the commonly adopted AI algorithms such as artificial neural networks, gradient boosting, random forest, and clustering. We discussed their specific applications in field development optimization and how they are combined with the classical optimization algorithms such as genetic algorithm, differential evolution, and particle swarm optimization.

关键词： field development optimization Well placement optimization Well control optimization field production strategy Artificial intelligence Machine learning

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field development optimization using a sequence of surrogate treatments

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COMPUTATIONAL GEOSCIENCES 2021年第1期25卷 35-65页

作者： de Brito, Daniel U. Durlofsky, Louis J. Stanford Univ Dept Energy Resources Engn Stanford CA 94305 USA Petroleo Brasileiro SA Petrobras Brazil

field development optimization, in which well configuration, well types, and well controls are determined, represents a computationally demanding mixed integer nonlinear programming problem. Such problems may require very large numbers of function evaluations, and if each of these corresponds to a detailed flow simulation, the optimization can become intractable. In this paper, we incorporate a set of surrogate treatments (STs) into the field development optimization problem. The basic ST is a variant of a recently developed surrogate procedure for optimizing well rates. It entails the solution of two optimization problems that both involve simplified physics (unit-mobility ratio displacement) and can be solved very efficiently. In the first problem, we find optimal well-rate ratios (i.e., the fraction of total injection or production allocated to each well), while in the second problem we determine optimal overall field injection and production rates. This ST is incorporated into a particle swarm optimization (PSO) framework. Three treatments are considered for subsequent optimization steps. All of these approaches involve full-physics simulations, and two of the methods entail the use of mesh adaptive direct search (MADS). The ST-based procedures are evaluated for two different 3D problems involving waterflood (with mobility ratios of 2 and 5) and water-alternating-gas (WAG) injection. The surrogate treatments are compared with standard approaches involving PSO, MADS, and a PSO-MADS hybrid. Extensive optimization results demonstrate that the ST-based methods provide consistent improvement in optimizer performance. For example, in the WAG case, the ST-based approach gives an optimal net present value that is 3.2% higher than that achieved using standard PSO-MADS, while also providing a 2.4 x computational speedup.

关键词： field development optimization Joint optimization Surrogate model Reservoir simulation

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fieldOpt: A powerful and effective programming framework tailored for field development optimization

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COMPUTERS & GEOSCIENCES 2020年第0期135卷 104379-000页

作者： Baumann, Einar J. M. Dale, Stein, I Bellout, Mathias C. NTNU Dept Geosci & Petr SP Andersens Veg 15a N-7031 Trondheim Norway Ceetron Solut AS Trondheim Norway

Petroleum field development involves critical decisions such as well location, well completion design and optimal control setting that have a significant impact on revenues and costs. These decisions are associated with a large degree of engineering effort that commonly involves time-consuming reservoir simulations to compute the performance of different field development scenarios. Increasingly within the petroleum industry, software tools and optimization methodology are developed and implemented to support and augment the various decision-making processes. The overall aim of these tools is to increase productivity and improve decision quality. Within this context, this work introduces an open-source, extensible, tailor-made programming framework: fieldOpt. fieldOpt's primary purpose is rapid prototyping and testing of optimization procedures to solve critical field development problems. The framework is implemented in C++ and provides an efficient integration of mathematical optimization procedures with reservoir simulation. fieldOpt's modular architecture and use of object-oriented programming allow the users to adapt and extend the code with ease. The architecture has proven successful in facilitating new optimization algorithms, new use cases, and new methodology for optimization with minimal effort and change to internal data structures and data flow. Three use cases are presented to demonstrate the optimization capabilities of fieldOpt as well as the flexibility and ease-of-use of the software regarding configuring various optimization procedures to solve a range of field development problems. The three use cases presented optimize on well control, well completion design and well placement parameters, respectively. For each case, both the configuration of the algorithms and problems within fieldOpt as well as the final solution and performance of the different optimization procedures are discussed. In all three cases, fieldOpt was able to find significant im

关键词： Programming framework field development optimization C plus

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Compact formulations for efficient early-phas e field development optimization of multi-reservoir fields

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COMPUTERS & CHEMICAL ENGINEERING 2021年 150卷 107319-107319页

作者： Lei, Guowen Silva, Thiago Lima Stanko, Milan NTNU Dept Geosci & Petr N-7031 Trondheim Norway

A compact formulation has been developed to efficiently optimize early-stage field development planning of multi-reservoir fields. The proposed formulation is a mixed-integer linear programming model which employs piecewise-linear functions to approximate the model non-linearities. The project economic value is maximized by optimizing the production allocation and the drilling schedule. The field production profiles are estimated with production potential curves calculated from simulated data of an integrated reservoir and surface facilities model. The novelties of this work are: a scalable model for the well combination selection, a logarithmic piecewise-linear model to approximate the well production potential curves, and the modeling and solution of realistic field development optimization problems. Through simulation analysis of a real field case study, the logarithmic and standard SOS2 formulations are compared in terms of computational performance and accuracy. The results show that the logarithmic formulation has significantly reduced the computational time and achieved improved accuracy over SOS2. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： field development optimization Mixed-Integer linear programming Piecewise-Linear functions

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field development optimization using a cooperative micro-particle swarm optimization with parameter integration schemes

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2019年 183卷 106416-000页

作者： Kim, Joonyi Kang, Byeongcheol Jeong, Hoonyoung Choe, Jonggeun Seoul Natl Univ Dept Energy Syst Engn Seoul 08826 South Korea

Non-gradient-based methods can be efficient to find optimal solutions for discrete decision variables of oil field development such as drilling schedule, number of wells, well locations, well types and its conversion times, and operational settings. Particle swarm optimization (PSO), which is a population-based stochastic global search method, has gained considerable popularity in optimization because it has simple and fast search using the speed of a few particles. However, its application to optimal oil field development is still computationally challenging due to many wells, reservoir heterogeneity, discrete variables, and nonlinear constraints. In this study, we introduce cooperative micro-particle swarm optimization (COMPSO), which will enhance the optimization efficiency of PSO for high-dimensional and complex problems by breaking a high-dimensional problem into low-dimensional subcomponents and using a small number of populations. We also propose an advanced parameterization of the decision variables for the efficient optimization of field development. A large 2-dimensional (2D) synthetic field and the 3D PUNQ-S3 benchmark model are used to demonstrate the efficiency of COMPSO and our parameterization schemes. The proposed method shows faster convergence and higher net present value (NPV) solutions than PSO. In the two examples, COMPSO only requires 39% and 16% of reservoir simulation runs to reach the same NPV compared with PSO, respectively. Our parameterization schemes additionally save 56% and 18% of the number of reservoir flow simulations of the COMPSO in the two examples, respectively.

关键词： field development optimization Drilling time Well conversion Cooperative micro-particle swarm optimization (COMPSO) Parameterization

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field development optimization in mature oil reservoirs using a hybrid algorithm

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2017年 156卷 41-50页

作者： Yang, Hyungjun Kim, Junyi Choe, Jonggeun Seoul Natl Univ Dept Energy Syst Engn Seoul 08826 South Korea

Many optimization schemes have been proposed to simultaneously optimize various variables such as well locations, well operation schedules, well types, and the number of wells. However, most of these approaches often focused on fixed well type without considering conversion of existing wells. This paper proposes a new optimization for mature oil field development. Since converting from producers to injectors is a common practice in mature oil field, we have to optimize simultaneously type conversion schedules of all existing producers and infill wells as well as the number of infill wells, their locations, and operation schedules. We propose a new hybrid algorithm, which combines differential evolution (DE) algorithm and mesh adaptive direct search algorithm (MADS) to solve our optimization task. By considering well type conversion, it will increase the complexity of searching space but provide more realistic and optimal development plan. We demonstrate it in 2D synthetic and 3D PUNQ-S3 reservoirs for optimal field development. The proposed optimization considering well type conversion provides higher net present value than the fixed well type optimization in the both cases. The hybrid algorithm also shows better search performances than DE and MADS algorithms. Thus, we conclude that consideration of well conversion schedules is necessary for economical field development scenarios in mature oil fields.

关键词： field development optimization Well type conversion schedule Mature oil reservoir Differential evolution (DE) Mesh adaptive direct search (MADS) Hybrid algorithm

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A two-stage optimization strategy for large-scale oil field development

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optimization AND ENGINEERING 2022年第1期23卷 361-395页

作者： Nasir, Yusuf Volkov, Oleg Durlofsky, Louis J. Stanford Univ Dept Energy Resources Engn Stanford CA 94305 USA

The optimization of the locations of a large number of wells in an oil or gas field represents a challenging computational problem. This is because the number of optimization variables scales with the maximum number of wells considered. In this work, we develop and test a new two-stage strategy for large-scale oil field optimization problems. In the first stage, wells are constrained to lie in repeated patterns, and the reduced set of optimization variables defines the pattern type and geometry (e.g., well spacing, orientation). For this component of the optimization, we introduce several important modifications, including optimization of the drilling sequence, to an existing well-pattern optimization procedure. The solutions obtained in the first stage are then used to initialize the second stage optimization. In this stage we apply comprehensive field development optimization, where the well location, type (injection or production well), drill/do not drill decision, completion interval for 3D models, and drilling time variables are determined for each well. Pattern geometry is no longer enforced in this stage. Specialized treatments (consistent with actual drilling practice) are introduced for cases where multiple geomodels, used to capture geological uncertainty, are considered. In both stages optimization is achieved using a particle swarm optimization-mesh adaptive direct search (PSO-MADS) method. The two-stage procedure is applied to 2D and 3D models corresponding to different geological scenarios. Both deterministic and geologically uncertain systems are considered. optimization results using the new procedure are shown to clearly outperform those from the single-stage comprehensive field development optimization approach. Specifically, for the same number of function evaluations, the two-stage treatment provides net present values that exceed those of the single-stage approach by about 15-18% for the cases considered. This suggests that this optimization str

关键词： field development optimization Well-pattern optimization Well placement Drilling schedule Realization-by-realization well completion

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Formulations for automatic optimization of decommissioning timing in offshore oil and gas field development planning

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COMPUTERS & CHEMICAL ENGINEERING 2022年 165卷

作者： Lei, Guowen Stanko, Milan Silva, Thiago Lima Norwegian Univ Sci & Technol NTNU Dept Geosci & Petr N-7031 Trondheim Norway SINTEF Ind Dept Sustainable Energy Technol NO-7465 Trondheim Norway

A mathematical programming formulation has been developed to optimize the drilling program, the pro-duction allocation, and the decommissioning time in early-stage field development planning. Various abandonment constraints are considered in the optimization model when searching for the best de-commissioning time for the long-term field development strategy. The model is tested on a real field development planning consisting of two reservoirs produced with subsea wells to an offshore facility. The novelty of this work is the utilization of mathematical programming to formulate and solve early -stage field development planning considering the variable decommissioning time. The results show that considering a variable decommissioning time in early-stage field development planning yields improved economic margins. Further, it provides operators a decision-support tool with enhanced capabilities of forecasting the best decommissioning time, improving the forecasts for processing and production plan-ning, expected revenue and lifetime of facilities, and anticipating and preparing for field abandonment in advance. The optimization model is capable of determining the drilling and production schedules, and the optimal decommissioning time that yielded the maximum net present value for the project.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： field development optimization Optimal decommissioning strategy Variable abandonment timing Mathematical programming Uncertainty analysis

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Kriging-boosted CR modeling for prompt infill drilling optimization

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Petroleum 2024年第1期10卷 39-48页

作者： Elizaveta S.Gladchenko Anna E.Gubanova Denis M.Orlov Dmitry A.Koroteev Skolkovo Institute of Science and Technology Russian Federation Digital Petroleum LLC Russian Federation

The capacitance-resistance model(CRM)has been a useful physics-based tool for obtaining production forecasts for ***,the model's limitations make it difficult to work with real field cases,where a lot of various events *** events often include new well commissioning(NWC).We introduce a workflow that combines CRM concepts and kriging into a single tool to handle these types of events during history ***,it can be used for selecting a new well placement during infill *** make the workflow even more versatile,an improved version of CRM was *** takes into account wells shut-ins and performed workovers by additional adjustment of the model *** preliminary re-weighing and interpolating these coefficients using kriging,the coefficients for potential wells can be *** approach was validated using both synthetic and real datasets,from which the cases of putting new wells into operation were *** workflow allows a fast assessment of future well performance with a minimal set of reservoir *** way,a lot of well placement scenarios can be considered,and the best ones could be chosen for more detailed studies.

关键词： Capacitance-resistance model(CRM) field development optimization Infill drilling Interwell connectivity Kriging New well commissioning(NWC)

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Integrated optimization of well placement and perforation layer selection using a modified dung beetle algorithm

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2024年 240卷

作者： Li, Shanshan Feng, Qihong Zhang, Xianmin Liu, Chen Zhou, Wensheng Wu, Kuankuan China Univ Petr East China Sch Petr Engn Qingdao Shandong Peoples R China Shandong Inst Petr & Chem Technol Dongying Shandong Peoples R China State Key Lab Offshore Oil Exploitat Beijing Peoples R China CNOOC Res Inst Co Ltd Beijing Peoples R China

In the oilfield development process, a proper well pattern plays a crucial role in improving oil recovery and economic benefits. However, traditional well pattern optimization mainly focuses on two-dimensional plane well placement, overlooking the spatial alignment of perforated layers and well placements. This oversight presents significant challenges, especially in meeting the demands for fine development of high water-cut oilfields. Therefore, this paper introduces an innovative approach to well pattern optimization, termed integrated well pattern optimization (WPO-I), which couples well placements with perforation positions. By maximizing net present value, a novel mathematical model for well pattern optimization is established, considering the location of infill wells, the status and type of existing wells, and the perforation position of working wells as cooptimization variables. Besides, an improved discrete dung beetle optimization (IDDBO) algorithm is developed, utilizing multi-layer integer coding and an opposition-based learning strategy. To evaluate the proposed method's performance, the Egg model is used to compare it with five optimization algorithms. Furthermore, this study assesses the development effects of conventional plane well placement optimization (WPO-P) compared to WPO-I. The results highlight the distinct advantages of the IDDBO algorithm in solving discrete nonlinear programming problems. Particularly, the WPO-I method enables spatial matching of the well pattern with reservoir heterogeneity and remaining oil distribution, effectively utilizing spatial residual oil. This method provides a new development optimization strategy in mature oilfields, especially for fine adjustment of well patterns during the secondary recovery stage of water flooding.

关键词： field development optimization Well pattern optimization Perforation position Dung beetle optimization algorithm Integrated optimization Water flooding reservoirs

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