Revisiting Troubleshooted Drill Stem Test: Methodological Framework Incorporating Artificial Intelligence

Abstract

Abstract Drill Stem Testing (DST) plays a crucial role in the petroleum industry, particularly in understanding a formation's behavior under production conditions. DST is instrumental in identifying critical reservoir engineering parameters such as permeability, skin factor, anisotropy, and vertical connectivity. These techniques are crucial for understanding reservoir behavior, optimizing production strategies, and making informed decisions regarding reservoir development. Despite its significance, DST faces challenges, primarily relating to the accuracy and reliability of the data obtained. The integrity of the data collected during DST is crucial, as inaccurate data, possibly resulting from fluctuated readings, can lead to suboptimal field development decisions. Many software offers pressure tie options to avoid errors if the data is not precise and reliable. Yet, the challenge is in the selection of effective data processing and analysis. The current study offers a detailed evaluation of DST data, using Kappa Software and analytical models, to assess permeability, pressure, well performance, and reservoir characteristics. The study incorporates the potential integration of Artificial Intelligence (AI) to enhance pressure reports as a preprocess analysis tool. AI applications could revolutionize DST data interpretation by quickly processing large datasets, identifying patterns, and providing accurate reservoir estimates. The methodological framework offers time reduction and improved noise filtering which leads to enhanced predictive insights into reservoir behavior. The use of AI in DST analysis promises a better use of noisy and troubleshooted DST data.