Optimizing Selection of Lateral-Re-Entry Wells Through Data-Driven Analytics

Fig. 1—Aggregated fuzzy-confidence map.

A new intelligent model that successfully learns from high-dimensional data and effectively identifies high-production areas and optimum lateral-re-entry candidates is presented. The model is entirely data driven and uses Wang-Mendel (WM) rules extraction, fuzzy logic (FL), pattern recognition, and Voronoi mapping. The authors applied their model to a large field with thousands of wells and multiple production layers, finding that it outperformed previous methodologies significantly.

Introduction

This paper demonstrates the application of data-driven-modeling technology to a relatively new domain, petroleum engineering—in particular, to ­production-opportunity identification and well-work optimization.

The Kern River field is the single largest producing onshore heavy-oil asset in North America. The structure is homoclinal, dipping southwest into the basin, with nine distinctive productive formations. The field has been produced with vertical wells through a combination of primary recovery and thermal enhanced recovery. The horizontal-well program was initiated in the Kern River field in 2007; because of its success, the program witnessed a significant growth year after year. An intelligent approach using fuzzy logic was introduced in 2012, which allowed identification of new ­horizontal-well opportunities previously missed by conventional methodology. More than 500 horizontal wells have been drilled in the field. This large number of wells provided an ideal data set for extracting reservoir knowledge for future optimization.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 170702, “Optimizing the Selection of Lateral-Re-Entry Wells Through Data-Driven Analytics,” by Andrei S. Popa, SPE, Chevron, prepared for the 2014 SPE Annual Technical Conference and Exhibition, Amsterdam, 27–29 October. The paper has not been peer reviewed.
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Optimizing Selection of Lateral-Re-Entry Wells Through Data-Driven Analytics

01 May 2015

Volume: 67 | Issue: 5

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