Optimal Well Placement Based on Artificial Neural Network Incorporating the Productivity Potential

Abstract This study analysed groundwater productivity potential (GPP) using three different models in a geographic information system (GIS) for Okcheon city, Korea. Specifically, we have used variety topography factors in this study. The models were based on relationships between groundwater productivity (for specific capacity (SPC) and transmissivity (T)) and hydrogeological factors. Topography, geology, lineament, land-use and soil data were first collected, processed and entered into the spatial database. T and SPC data were collected from 86 well locations. The resulting GPP map has been validated in under the curve analysis area using well data not used for model training. The GPP maps using artificial neural network (ANN), frequency ratio (FR) and evidential belief function (EBF) models for T had accuracies of 82.19%, 81.15% and 80.40%, respectively. Similarly, the ANN, FR and EBF models for SPC had accuracies of 81.67%, 81.36% and 79.89%, respectively. The results illustrate that ANN models can be useful for the development of groundwater resources.

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Data-driven modeling to optimize the injection well placement for waterflooding in heterogeneous reservoirs applying artificial neural networks and reducing observation cost

Energy Exploration & Exploitation ◽

10.1177/0144598720927470 ◽

2020 ◽

Vol 38 (6) ◽

pp. 2413-2435 ◽

Cited By ~ 1

Author(s):

Xinwei Xiong ◽

Kyung Jae Lee

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Injection Well ◽

Data Driven ◽

Well Placement ◽

Heterogeneous Reservoirs ◽

Modeling Approach ◽

Artificial Neural ◽

Well Data ◽

Data Driven Modeling

Secondary recovery methods such as waterflooding are often applied to depleted reservoirs for enhancing oil and gas production. Given that a large number of discretized elements are required in the numerical simulations of heterogeneous reservoirs, it is not feasible to run multiple full-physics simulations. In this regard, we propose a data-driven modeling approach to efficiently predict the hydrocarbon production and greatly reduce the computational and observation cost in such problems. We predict the fluid productions as a function of heterogeneity and injection well placement by applying artificial neural network with small number of training dataset, which are obtained with full-physics simulation models. To improve the accuracy of predictions, we utilize well data at producer and injector to achieve economic and efficient prediction without requiring any geological information on reservoir. The suggested artificial neural network modeling approach only utilizing well data enables the efficient decision making with reduced computational and observation cost.

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Development of Well Placement Optimization Model using Artificial Neural Network and Simulated Annealing

Journal of the Korean Institute of Gas ◽

10.7842/kigas.2015.19.1.28 ◽

2015 ◽

Vol 19 (1) ◽

pp. 28-37

Author(s):

Tae-Sung Kwak ◽

Ji-Hun Jung ◽

Dong-Kwon Han ◽

Sun-Il Kwon

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Simulated Annealing ◽

Optimization Model ◽

Well Placement ◽

Placement Optimization ◽

Well Placement Optimization ◽

Artificial Neural

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A Study on Production Well Placement for a Gas Field using Artificial Neural Network

Journal of the Korean Institute of Gas ◽

10.7842/kigas.2013.17.2.59 ◽

2013 ◽

Vol 17 (2) ◽

pp. 59-69

Author(s):

Dong-Kwon Han ◽

Il-Oh Kang ◽

Sun-Il Kwon

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Production Well ◽

Gas Field ◽

Well Placement ◽

Artificial Neural

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Well-placement optimisation using sequential artificial neural networks

Energy Exploration & Exploitation ◽

10.1177/0144598717729490 ◽

2017 ◽

Vol 36 (3) ◽

pp. 433-449 ◽

Cited By ~ 12

Author(s):

Ilsik Jang ◽

Seeun Oh ◽

Yumi Kim ◽

Changhyup Park ◽

Hyunjeong Kang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Global Solution ◽

Search Space ◽

Superior Performance ◽

Training Dataset ◽

Well Placement ◽

Artificial Neural

In this study, a new algorithm is proposed by employing artificial neural networks in a sequential manner, termed the sequential artificial neural network, to obtain a global solution for optimizing the drilling location of oil or gas reservoirs. The developed sequential artificial neural network is used to successively narrow the search space to efficiently obtain the global solution. When training each artificial neural network, pre-defined amount of data within the new search space are added to the training dataset to improve the estimation performance. When the size of the search space meets a stopping criterion, reservoir simulations are performed for data in the search space, and a global solution is determined among the simulation results. The proposed method was applied to optimise a horizontal well placement in a coalbed methane reservoir. The results show a superior performance in optimisation while significantly reducing the number of simulations compared to the particle-swarm optimisation algorithm.

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