Screening Method To Select Horizontal-Well Refracturing Candidates in Shale-Gas Reservoirs

Abstract

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 144032, ’A Novel Screening Method for Selection of Horizontal- Refracturing Candidates in Shale-Gas Reservoirs,’ by Shekhar Sinha, SPE, and Hariharan Ramakrishnan, SPE, Schlumberger, prepared for the 2011 SPE North American Unconventional Gas Conference and Exhibition, The Woodlands, Texas, 14-16 June. The paper has not been peer reviewed. A method was developed to screen potential horizontal-well-refracturing candidates rapidly by use of production performance and completion-data analysis. Integration of initial hydraulic-fracture-completion details augments the process and helps screen understimulated wells in different production classes. To accomplish this screening, an index called a “completion index” was defined after analysis of the completion parameters, production behavior, and their interrelationship. Introduction Restimulation of existing wells represents a vast unexploited resource in tight formations. In 1996, the Gas Research Institute, now the Gas Technology Institute (GTI), investigated the potential for natural-gas-production enhancement by use of restimulation in the USA (onshore, lower 48 states). The report indicated that the potential was substantial (more than 1 Tcf of reserves in 5 years), particularly in the tight gas sands of the Rocky Mountain, midcontinent, and south Texas regions. The study also stated that 85% of the restimulation potential for a field exists in 15% of the wells. Hence, the key to any successful restimulation program is being able to identify that 15 to 20% of the total well population that represents high potential for restimulation success. However, it also was determined that industry’s current experience with restimulation is mixed, and that considerable effort is required in candidate selection, problem diagnosis, and treatment design/implementation for a program to be successful. The GTI study investigated three main classes of candidate-selection methods: production-performance comparisons, pattern-recognition-technology/virtual-intelligence methods, and production-type-curve matching. The study concluded that although virtual-intelligence methods were relatively better compared to production type curves, no universal method exists that enables selecting restimulation candidates across different geologic settings. Use of production statistics alone was the least-effective process.