An Innovative Approach to Forecasting Matrix Stimulation Treatment Results: A Case Study

Abstract

Abstract Successful matrix stimulation engineering depends on knowledge of formation damage and its extent in causing the well not to produce to its potential. Stimulation design is a complex procedure as formation properties need to be honored when designing for sandstones, whereas for carbonates reservoirs, the presence of secondary porosity along with matrix requires a somewhat different approach. Operating companies have positive expectations for matrix treatments as they see it as a cost effective, efficient and safe approach of intervention to restore or stimulate production of a well that has formation damage. In the oil and gas industry, diverse software applications are available to model matrix stimulation treatments but validity of such models depend on their robustness, starting with input data; i.e. formation characteristic such as permeability, porosity, pressures, temperature, skin-damage and its extent have strong impacts on the simulation results. Not very often is all of this information available; and if it exists, sometimes it's not up to date or questions may arise about the accuracy. This leads to operators to question how valid the design is and if it will allow them to meet their goals or not? This paper describes an innovative approach that has been implemented to establish an effective stimulation design and forecast results based on execution parameters. The idea consists in using real-time downhole pressure and temperature measurements, along with Distributed Temperature Sensing (DTS) acquired during matrix stimulation execution. The downhole data is utilized to calibrate the formation and fluid characteristics via pressure matching which provides ability to determine how much of actual skin is reduced. This result is then incorporated to a reservoir model to forecast and evaluate the potential of the post stimulation production results.