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Machine Learning Overcomes Challenges of Selecting Locations for Infill Wells

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 average 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.

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 The paper has not been peer reviewed.
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Machine Learning Overcomes Challenges of Selecting Locations for Infill Wells

01 October 2018

Volume: 70 | Issue: 10

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