Abstract

This paper discusses the use of continuous scratch testing for evaluation of rock heterogeneity and effective correction and calibration of mechanical properties predictions using wire line logging. Understanding the vertical variability of rock properties (strength and moduli) along the reservoir thickness is of primordial importance for sanding prediction analysis. Logging measurements (GR, Sonic, Density and Porosity) are traditionally used to identify troublesome intervals, to select sections for laboratory testing and for extrapolating laboratory data to untested reservoir sections. However, depending on the scale of the reservoir heterogeneity, the log measurements, by providing integrated values over their window of vertical resolution, can considerably underestimate the strong reservoir sections and overestimate the weak reservoir sections. This leads to misrepresentation of critical weak layers with high potential for sanding and potentially to large errors in the completion strategy with costly long-term consequences. Corrections are thus necessary and are traditionally based on simple unconfined compression (UCS) tests conducted sparsely or at high density, varying from operator to operator. This paper presents a method for log-correction based on continuous core scratching in the laboratory and shows that the methodology is effective in evaluating changes in properties with high accuracy and a resolution of at least 1 cm.

Introduction

Rock mechanical properties are fundamental to analysis of wellbore stability, sanding potential, mud weight requirements, casing collapse, compaction potential and many other problems related with the short- and long term mechanical stability of the well and the reservoir. These predictions, when correct, may help saving millions of dollars in drilling and completions costs, and may allow preventing long term and costly consequences. A particular problem in petroleum geosciences is that rock mechanics data is hard to come by and limited to small sections of the reservoir. The lack of data and, when available, the lack of significant volume representation of these data, has frustrated many investigators and has led them to pursue alternative avenues (e.g., correlations, statistical estimates) to obtain rock mechanical properties. In petroleum-related rock mechanics, computational capabilities for numerical simulations of wellbore stability, sanding potential, reservoir compaction, casing failure during production and other pressing mechanical problems have evolved into sophisticated tools whose demands for field data (e.g., in-situ stress, rock properties, coupled hydraulic-thermal-mechanical properties, and the lateral and vertical variability of these properties across a reservoir) far exceeds the industry's capacity to provide it. Thus engineers struggle to develop alternative sources of data via correlations, inferences from seismic prospecting, inferences from log measurements, or by developing knowledge-based criteria to guess reasonable values. However, lacking good quality data, rock mechanics predictions are weak and most importantly, the industry confidence for implementing them to field applications is low. Thus rock mechanics analysis, limited by lack of data, is a service that does not fulfill its potential for minimizing well construction costs or preventing, long term, production-related problems.

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