Abstract
In gas storage wells, many different types of formation damage can occur that dramatically curtail injection and withdrawal rates. Some of these damage mechanisms are similar to producing wells (mud/cement damage during drilling, completion problems, etc.); however, some types of damage are more specific to gas injection and storage (bacterial growth, contaminants, etc.). All these different damage mechanisms require different stimulation treatment methods and fluids to increase injectivity and deliverability. However, diagnosing the correct type of formation damage is not a simple task. The process requires gathering specific types of data and interpreting the results. Correct diagnosis of the actual damage mechanism(s) and design of the appropriate treatment requires expertise and experience.
Over the years, numerous studies have been performed on formation damage, and a vast amount of experience has been obtained on the design of stimulation methods and fluids to remove damage. However, this knowledge and experience is not always available or accessible to engineers dealing with stimulation design, especially with the unique types of problems associated with gas storage reservoirs.
This paper describes a comprehensive computer model designed for gas storage wells to help engineers diagnose formation damage and select the best stimulation treatment. The model combines domain knowledge bases with the best available expertise using fuzzy logic and expert system technologies. After diagnosing the most likely formation damage mechanism(s), based upon input data, the program will select the best treatment method and recommend treatment fluids and additives for the stimulation.
