Practical Approach To Achieve Accuracy in Sanding Prediction
Authors: Denney Dennis 
Published: February 2015

This article, written by Technology Editor Dennis Denney, contains highlights of paper SPE 100944, "Practical Approach To Achieve Accuracy in Sanding Prediction," by K. Qiu, SPE, J.R. Marsden, SPE, J. Alexander, SPE, and A. Retnanto, SPE, Schlumberger, and O.A. Abdelkarim, SPE, and M. Shatwan, Agoco, prepared for the 2006 SPE Asia Pacific Oil & Gas Conference and Exhibition, Adelaide, Australia, 11–13 September.

Sand production can reduce oil production, cause erosion in downhole and surface facilities, require additional separation and disposal, and lead to significant economic loss. Precautionary, but unnecessary, sand prevention will result in unwarranted reduction in productivity. Overestimating or underestimating sanding risk increases the chances of serious economic loss. Reliable sanding-prediction analysis provides a basis for designs that achieve appropriate sand-management strategies and maximize economic production.

Introduction

The giant Messla field is in the southeast portion of Sirte basin in Libya, approximately 500 km southeast from Benghazi. The field has been producing for more than 30 years, and since the mid-1980s, some wells have suffered massive sanding, while others have not. A geomechanics and sanding study was initiated in 2005 to investigate this variation, to evaluate the severity of sanding risk in other wells, and to provide information and interpretations needed to design appropriate completions, maximize economical production, and optimize future reservoir management.

Sanding analyses were conducted by use of a proprietary sanding-prediction application. This application incorporates a novel analytical model that integrates a simple linear elastic analysis and then accounts for rock failure, plasticity effects, and scale effects. Information is from simple laboratory tests and the results of previous extensive numerical modeling and field validation. Significant factors contributing to sand production include stresses, rock strength, draw-down and depletion, and completion type and geometry so that the analysis achieves practicability and fit-for-purpose accuracy. The implementation allows modifying the analytical elastic calculations to achieve a more realistic and accurate prediction.

The remaining major uncertainties in the predictive capacity of the model arise from uncertainties of the input parameters rather than from the model itself. Therefore, to achieve the desired accuracy in the analyses and predictions, attention should focus on decreasing uncertainties of the input parameters.

Sanding-Prediction Approaches

There are three main sources of uncertainty in sanding prediction: the predictive model itself, the input data for the model, and processing uncertainties such as truncation and round-off errors. Reliable analyses are obtained by generating a well-constrained mechanical Earth model (MEM) to minimize uncertainties in the input data, by accounting for all the main influences, and by simplifying processing.

Empirical Approach. This popular approach relies on data collected from the field, possibly supplemented by laboratory experiments on cores. In most instances, only one or two parameters are used to assess sanding risk and to establish cutoffs for conditions of sanding or no sanding. For example, a cutoff depth or a cutoff compressional-slowness criterion might be used to determine whether or not sand control might be needed in a particular field.