The technical presentation “Integrating Calibrated Mud Gas Analysis into Formation Evaluation Workflows - Experience from a Norwegian Oil Field“ is presented by Maneesh Pisharat from SLB.
Abstract
Geosteering and well placement in mature reservoirs commonly rely on logging‑while‑drilling (LWD) measurements, which, despite providing high‑resolution petrophysical data, have in several cases led to incorrect or uncertain fluid type interpretations that were only verified through additional measurements or during production. In complex settings characterized by compartmentalization and dynamic fluid contacts, the ability to reliably distinguish between oil, gas, and mixed‑phase fluids is essential for effective reservoir management and real‑time operational decision‑making.
Advanced Mud Gas (AMG) analysis addresses this limitation by enabling continuous, real‑time evaluation of light hydrocarbon components (C1–C5) while drilling. To enhance data robustness and minimize operational bias, the workflow incorporates Extraction Efficiency Corrected (EEC) gas concentrations to account for surface extraction variability, alongside normalized total hydrocarbon concentrations (THCnorm) to mitigate the effects of drilling parameters such as rate of penetration and hole size.
Reservoir fluid types are interpreted using three established diagnostic gas ratios—Wetness, Balance, and Character—augmented by machine learning (ML) models trained on global datasets to predict compositional trends and fluid phase behavior. The integrated workflow is applied to a case study from a well drilled on the Norwegian Continental Shelf, demonstrating its effectiveness in a mature and infrastructure‑constrained environment.
Results indicate that the combined use of EEC‑corrected gas ratios and ML‑based predictive analytics significantly improves reservoir delineation and reduces uncertainty in fluid phase interpretation compared to conventional LWD‑only approaches. This methodology provides a reliable, scalable, and real‑time complement to traditional petrophysical workflows, enabling enhanced geosteering and informed decision‑making in mature reservoirs.
Bio
Maneesh Pisharat is a Surface Logging Domain Champion with SLB, where he works in formation evaluation through surface measurements. He provides technical leadership and support in advanced mud gas analysis and drilled cuttings evaluation. He has held a variety of surface logging roles across the Middle East, Asia, North America, and Europe, bringing broad international experience to his work. With over 25 years of expertise in surface logging, he has co-authored multiple technical papers and delivered technical courses for professional societies. His current interests focus on applying machine learning to enhance surface logging operations and workflows.