DrillAI, a pioneering company in the oil and gas industry, is at the forefront of innovation with its advanced Pore Pressure Prediction Module, a powerful tool leveraging machine learning and artificial intelligence techniques.

The Pore Pressure Prediction module has been used to estimate the pressure of fluids trapped in rock formations during drilling operations. these fluids can include oil, gas, and water. This module helps determine the pressure required to drill through these formations without causing damage.

By providing accurate pore pressure predictions, the module helps improve drilling efficiency, reduce costs, and enhance safety during oil and gas drilling operations.

Pore pressure, a pivotal parameter in the industry, is instrumental in understanding reservoir conditions and optimizing drilling programs. Furthermore, it forms the basis for establishing safe drilling practices.

DrillAI’s cutting-edge Pore Pressure Prediction Module harnesses both empirical equations and ML methods to accurately forecast pore pressure, all driven by well log data.

Our research draws upon a wealth of data collected from various wells.

In scenarios where comprehensive well logs pose challenges, particularly in offshore environments, DrillAI introduces innovative solutions. Our Gradient Boosting model for predicting acoustic wellbore logs, complemented by a physics-based model for pore pressure prediction, minimizes the impact of data limitations. This dual approach augments accuracy and reliability, making it an invaluable tool for pore pressure analysis.

Addressing Anomalously High Pressures

In addition to our core pore pressure prediction capabilities, DrillAI recognizes the importance of catering to reservoir blocks characterized by abnormally high pressures. While the industry has made significant strides in applying ML and AI to predict pore pressure, much of this research has concentrated on normal-pressure blocks.

DrillAI forward-looking approach extends to solving this gap by introducing methods tailored to these challenging scenarios.

Our methodology integrates various techniques, including the analysis of density–sonic velocity cross-plots and the Bowers method, to unravel the complexities of high-pressure formations. We attribute elevated pressures to factors such as hydrocarbon generation and employ these insights to calibrate pore pressure predictions. The result is a suite of intelligent prediction models that excel in accuracy and applicability.