Data-driven evolutionary algorithm for oil reservoir well-placement and control optimization

Chen, Guodong; Luo, Xin; Jiao, Jiu Jimmy; Xue, Xiaoming

doi:10.1016/j.fuel.2022.125125

Cited by 17 publications

(2 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies of well placement optimization using genetic algorithms (GAs) [10][11][12] as optimization algorithms have been carried out by several researchers using a variety of methods. A methodology is developed to deploy high-deviation wells and horizontal wells [13].…”

Section: Introductionmentioning

confidence: 99%

Global Genetic Algorithm for Automating and Optimizing Petroleum Well Deployment in Complex Reservoirs

Irawan,

Wayo,

Satyanaga

et al. 2024

Energies

View full text Add to dashboard Cite

Locating petroleum-productive wells using informed geological data, a conventional means, has proven to be tedious and undesirable by reservoir engineers. The former numerical simulator required a lengthy trial-and-error process to manipulate the variables and uncertainties that lie on the reservoir to determine the best placement of the well. Hence, this paper examines the use of a global genetic algorithm (GA) to optimize the placement of wells in complex reservoirs, rather than relying on gradient-based (GB) methods. This is because GB approaches are influenced by the solution’s surface gradient and may only reach local optima, as opposed to global optima. Complex reservoirs have rough surfaces with high uncertainties, which hinders the traditional gradient-based method from converging to global optima. The explicit focus of this study was to examine the impact of various initial well placement distributions, the number of random solution sizes and the crossover rate on cumulative oil production, the optimization of the synthetic reservoir model created by CMG Builder, CMOST, and IMEX indicated that using a greater number of random solutions led to an increase in cumulative oil production. Despite the successful optimization, more generations are required to reach the optimal solution, while the application of GA on our synthetic model has proven efficient for well placement; however, different optimization algorithms such as the improved particle swarm (PSO) and grey wolf optimization (GWO) algorithms could be used to redefine well-placement optimization in CMG.

show abstract

Section: Introductionmentioning

confidence: 99%

Global Genetic Algorithm for Automating and Optimizing Petroleum Well Deployment in Complex Reservoirs

Irawan,

Wayo,

Satyanaga

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…They also require a series of assumptions about the actual problem that may not match the actual situation, leading to inaccuracy and reduced reliability of the model. These are all issues that need to be addressed in current hydraulic fracturing projects [9][10][11][12].…”

mentioning

confidence: 99%

A Powerful Prediction Framework of Fracture Parameters for Hydraulic Fracturing Incorporating eXtreme Gradient Boosting and Bayesian Optimization

Liu,

Lei,

Weng

et al. 2023

Energies

View full text Add to dashboard Cite

In the last decade, low-quality unconventional oil and gas resources have become the primary source for domestic oil and gas storage and production, and hydraulic fracturing has become a crucial method for modifying unconventional reservoirs. This paper puts forward a framework for predicting hydraulic fracture parameters. It combines eXtreme Gradient Boosting and Bayesian optimization to explore data-driven machine learning techniques in fracture simulation models. Analyzing fracture propagation through mathematical models can be both time-consuming and costly under conventional conditions. In this study, we predicted the physical parameters and three-dimensional morphology of fractures across multiple time series. The physical parameters encompass fracture width, pressure, proppant concentration, and inflow capacity. Our results demonstrate that the fusion model applied can significantly improve fracture morphology prediction accuracy, exceeding 0.95, while simultaneously reducing computation time. This method enhances standard numerical calculation techniques used for predicting hydraulic fracturing while encouraging research on the extraction of unconventional oil and gas resources.

show abstract

Application of surrogate-assisted global optimization algorithm with dimension-reduction in power optimization of floating offshore wind farm

Song,

Shen,

Gao

et al. 2023

Applied Energy

View full text Add to dashboard Cite

Data-driven evolutionary algorithm for oil reservoir well-placement and control optimization

Cited by 17 publications

References 59 publications

Global Genetic Algorithm for Automating and Optimizing Petroleum Well Deployment in Complex Reservoirs

Global Genetic Algorithm for Automating and Optimizing Petroleum Well Deployment in Complex Reservoirs

A Powerful Prediction Framework of Fracture Parameters for Hydraulic Fracturing Incorporating eXtreme Gradient Boosting and Bayesian Optimization

Application of surrogate-assisted global optimization algorithm with dimension-reduction in power optimization of floating offshore wind farm

Contact Info

Product

Resources

About