Advanced Understanding of Matrix Stimulation Treatments Through In-Job and Post-Job Evaluation of Real-Time Downhole Measurements with Coiled Tubing: A Case Study

摘要

Matrix stimulation treatments executed with coiled tubing (CT) face various challenges in terms of design, execution, and evaluation. The design phase typically relies on information that is frequently poorly known (e.g., extent of damage). Treatment pumping schedules and fluid concentrations are often determined based on previous experience and accepted local practices. For the execution to be completed within a safe framework, the standard is to keep pumping pressures below the fracturing pressure. In some cases, tools like high pressure differential jetting nozzles are used to provide deeper penetration and lower breakdown pressures. The depths at which those tools are operated usually depend on a prior log interpretation. Finally, treatment evaluation is typically limited to the comparison of pre and post-stimulation wellhead pressures and rates. Over the past decade, numerical modeling has allowed the industry to address some of the design and evaluation challenges. Yet, the same question often remains: has the design been effectively executed and was the intervention successful? The answer depends on the choice of success criteria such as efficiency, safety, and economics. CT enabled with fiber optic telemetry-which consists of downhole gauges providing real-time data of pressure, temperature, gamma ray, and casing collar locator-has proven a game-changing technology with respect to treatment execution, improving both intervention efficiency and safety (Jacobsen et al. 2010). The provided measurements, along with the possibility to acquire distributed temperature surveys (DTS), have also shown to be the most effective solution for treatment evaluation to date. The case study presented here not only describes how CT with downhole sensors was used to optimize the acidizing treatment of an oil well producer and ensure its effective stimulation, but it also demonstrates how the real-time and DTS data were analyzed both during the intervention and through post-job numerical modeling, in order to refine the understanding of the well and that of its formation characteristics.