Determination of an infill well placement using a data-driven multi-modal convolutional neural network

Research on abnormal object detection in specific region based on Mask R-CNN

“…20,21 In the field of object recognition, CNN can effectively capture the deep semantic features of images, get a large number of representative feature information, and finally classify and predict the samples with higher accuracy. 22,23 With the continuous breakthrough of deep learning in the field of computer vision, R-CNN algorithm is a widely used object detection algorithm. However, due to the problem of repeated calculation of feature links, Fast R-CNN algorithm is proposed on the basis of R-CNN.…”

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

“…Surrogate models (SMs, also known as proxy models) are employed as an approximation method in the optimization process to reduce the cost of objective function evaluations when the underlying fullphysics model is expensive to simulate. Three main types of surrogate modeling approaches are commonly employed in the field development and control optimization problems: (1) physics-based approaches such as reduced order modeling (Van Doren et al, 2006;Cardoso and Durlofsky, 2010;Durlofsky, 2010;He and Durlofsky, 2014;Trehan and Durlofsky, 2016) or streamline-based simulation methods (Thiele and Batycky, 2003;Park and Datta-Gupta, 2011;Salehian and Çınar, 2019;Ushmaev et al, 2019), (2) Machine Learning (ML) techniques such as support vector machine (SVM) (Drucker et al, 1997;Guo and Reynolds, 2018;Panja et al, 2018;Zhang et al, 2021), Artificial Neural Network (ANN) (Jain et al, 1996;Güyagüler et al, 2002;Yeten et al, 2003;Golzari et al, 2015;Rahmanifard and Plaksina, 2019;Sabah et al, 2019;Sun and Ertekin, 2020;Enab and Ertekin, 2021;Gouda et al, 2021), Gaussian Process Regression (GPR) (Knowles, 2006;Zhang et al, 2009;Horowitz et al, 2013) methods, and (3) Deep Learning (DL) methods such as Convolutional Neural Network (CNN) (LeCun et al, 1998;Glorot et al, 2011;Hinton et al, 2012;Chu et al, 2020;Kim et al, 2020;Kim et al, 2021). Physics-based approaches can approximate the original reservoir behavior with lower-order equations to reduce the computational cost.…”

“…This lower accuracy is mainly due to disregarding spatial features (e.g. well location, type, and trajectory) in a large-scale problem and transforming the inputs to a 1D array (Chu et al, 2020). CNNs methods are an advanced form of ANNs that eliminate the issues associated with the conventional ML techniques by allowing the direct import of multi-dimensional data to the network (LeCun and Bengio, 1995;Behnke, 2003).…”

“…CNNs methods are an advanced form of ANNs that eliminate the issues associated with the conventional ML techniques by allowing the direct import of multi-dimensional data to the network (LeCun and Bengio, 1995;Behnke, 2003). This allows multi-dimensional input variables such as well locations and types of wells (i.e., injectors and producers) to be directly imported to the network without losing their spatial information providing greater SM prediction performance (Chu et al, 2020;Razak and Jafarpour, 2020). Standard feedforward CNNs are used to predict one, single output while Recurrent Neural Networks (RNNs) such as Long-Short Term Memory (LSTM) can predict time series (Gers et al, 2002;Hua et al, 2019;Sagheer and Kotb, 2019;Song et al, 2020).…”

Current Status and Prospects of Artificial Intelligence in the Oil and Gas Exploration and Production Business

“…Fig.5. Structure of CNN and its application to well placement optimization (modified fromChu et al, 2020).…”