Abstract

Variables affecting the performance of a fractured well (over that of the unstimulated well) have a large impact on fracture design. The reservoir permeability and, often, the horizontal permeability anisotropy are the important variables to measure at the pretreatment state. They generally control the optimum fracture length. Predictions of fracture permeability (affected by stresses and residue damage) and fracture height (affected by the interlayer stress contrast) must also be taken into consideration.

Evaluation of the fracture job requires measurements that would lead to the estimation of actual fracture height, fracture length, and fracture conductivity. These estimations allow the forecast of fractured well performance and the rationalization of the job design. This well performance analysis before and after stimulation should be an integral part of a properly engineered reservoir exploitation strategy.

Introduction

Hydraulic fracturing is the most common stimulation treatment practiced by the petroleum industry today. While estimates vary, it is a safe assumption that more than 70% of all wells in North America have been fractured. Since 1955, more than 3000 treatments per month have been performed.1

It is also a safe assumption that most of these treatments have not been optimized on the basis of the desired incremental performance from the well. There are two main reasons contributing to this:

  • Most reservoirs that are candidates for hydraulic fracturing have low permeabilities. Their pretreatment testing, intended to measure this permeability, either cannot be done because the well does not flow or the testing can be impractical because of an inordinately long duration.

  • The posttreatment interpretation is hindered by similar test duration considerations. In addition, the fracture variables that are more desirable to measure (fracture length and conductivity) may be inaccessible within certain fracture conductivity ranges. Furthermore, the calculated apparent values may be substantially different from the actual ones. While these discrepancies can be reconciled,2 they often require costly additional measurements.

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