Using Integrated Information to Optimizing Matrix Acidizing
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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.
