Abstract

The technology of matrix acidizing has advanced dramatically over the last 30 years. Models have been developed to help understand and design the process, and methods have been applied in the field to monitor and evaluate treatments to achieve stimulation results as expected. Despite these advances, the success rate of acidizing is still low (estimated to be 50% –70%), particularly in sandstones. When an acid treatment fails, not only will it waste the cost of the treatment, but also may possibly cause further damage to the well and lead to more severe problems.

Acidizing success strongly depends on the susceptibility of nearwell formation damage to removal by the acid solution. Thus, the nature of the damaging material and the depth to which the damage penetrates into the formation are critical properties to the success of acidizing treatments. Predicting the outcome of a treatment using models of the process depends most strongly on the description of the damaged region provided to the model. The dilemma we face is that we seldom have a detailed knowledge of the damage, but instead only a global measurement of the skin factor of the well. In this paper, we present an approach to improve the success rate of matrix acidizing by using integrated formation damage models, sandstone acidizing models, skin evolution models, and well production information. The theoretical prediction of the well response to an acid treatment is compared with a skin analysis result obtained during the treatment. Problems with inappropriate treating fluids can be identified clearly with increased skin factor during certain injection periods. Damage depth and damaged permeability can be re-evaluated from the characteristics of the well response to the acid. The skin analysis result can be confirmed with the well production or injection data after acidizing. Using the improved description of the damage obtained from the skin analysis of treated wells, the simulation model can then be used to redesign acidizing treatments to achieve better results.

Introduction

Acidizing treatments are often used in improving production and injection well performance. The relatively low cost and simpler procedure compared with hydraulic fracturing make acidizing an attractive practice in well stimulation. The success of acidizing treatments strongly depends on reservoir and formation damage information (candidate identification), acid and additives selection, and treatment quality control. Since there is often limited knowledge of the near wellbore damage qualitatively (the causes of the damage) and quantitatively (permeability reduction and damage depth), in many cases the acidizing design is based on uncertain information. In general, injected acid can penetrate to a very short distance in the formation, and acidizing can only remove the damage near the wellbore. When the damage depth is incorrctly estimated in the design, the acid treatment may fail in the field. In this paper, we will discuss how the information obtained from well stimulation can help to further understanding of damage in the well, and therefore to improve acidizing treatment design. The approach presented is to utilize all available models and information in a consistent manner.

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