Machine Learning Overcomes Challenges of Selecting Locations for Infill Wells

Abstract

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 190101, “Infill-Well Location-Selection Procedures in Lost Hills Using Machine Learning,” by T.H. Kim, SPE, D.J. Crane, and E.F. Grijalva, SPE, Chevron, prepared for the 2018 SPE Western Regional Meeting, Garden Grove, California, USA, 22–27 April. The paper has not been peer reviewed. For most conventional reservoirs, numerical simulation is successful in forecasting and extracting valuable information regarding optimal locations for new wells. The results of numerical simulations for the Lost Hills field, however, were not successful because of the special characteristics of its diatomite reservoirs—low permeability but high porosity, weak rock strength, and strong imbibition. Machine learning (ML) has been considered because it does not require specific physical models but can provide good estimations with enough data. Introduction The Lost Hills field is approximately 45 miles northwest of Bakersfield, California, USA. It was discovered in 1910, and hydraulic fracturing was introduced in the late 1970s. Waterflooding was introduced in 1992 and has become the main production method. The main reservoir rock type is diatomite, which is formed by the accumulation of diatoms, single-cell organisms. The aver-age pay thickness is approximately 800 to 1,000 ft, and depth to the reservoir base is approximately 2,000 ft. The estimated original oil in place is more than 2 billion bbl. ML Algorithms and Voronoi Diagram K-Means Clustering. K-means clustering is one type of unsupervised learning algorithm. As opposed to supervised algorithms, unsupervised learning algorithms do not need answers supplied. K-means clustering algorithms identify the members of each cluster by iterating the following steps: Generate the centroid of each of the clusters randomly. The number of clusters, K, is a user input. Each data point is assigned to its nearest centroid so that K clusters are created. The new centroid of each cluster is updated so that the Euclidian distances to the centroid are minimized. Repeat Steps 2 and 3 until convergence has been reached. Support Vector Machine (SVM). SVM is a supervised ML algorithm. For supervised learning algorithms, the answers must be supplied to train and build a predictive model. SVM has two main applications, classification and regression, and classification is used in this study. SVM builds a hyperplane or set of hyperplanes that separate data into groups (classification) or form a regression line; a hyperplane is a subspace of one dimension less than its ambient space (e.g., a plane in 3D space). Fig. 1 shows an example of classification in 2D space. The red line is a hyperplane set to have the maximum gap between two classes. Data vectors in circles are borderline members and are called support vectors. When building a hyperplane is difficult, data are mapped into a higher-dimensional space to facilitate finding a hyperplane.