Artificial Intelligence Enhanced Prediction of Water Loading in Gas Wells: A Data-Driven Alternative to Turner's Model Aashish Dheeraj Dadwani, Gaurav Hazarika Society of Petroleum Engineers Adipec 2025, 2025 This study aims to develop an AI-enhanced, optimized empirical model for predicting critical gas rates to prevent water loading. By integrating real–field data and advanced machine learning techniques, a new Turner–style equation will be formulated. The model will dynamically adapt to varying well conditions, outperform traditional methods, and enable proactive, real–time management of deliquification strategies in complex environments. High- resolution well datasets, including bottomhole pressure, wellhead pressure, gas properties, and production rates, were extracted of a well X were taken and rigorously preprocessed. Variance analysis and permutation entropy were applied to detect early–stage flow regime transitions associated with liquid loading onset. Polynomial feature transformations captured nonlinear and cross–variable interactions among key operational parameters. A supervised learning framework with polynomial regression achieved 92% accuracy in predicting critical gas rates, representing a 28% improvement over conventional Turner–based methods. The final Turner–style AI–enhanced model dynamically adapts to changing multiphase flow regimes , enabling proactive, real–time optimization of deliquification strategies in Production systems.The polynomial regression approach , incorporating second–order and interaction terms between flowing bottomhole pressure (Pwf) and gas density (ρg),enabled the model to accurately capture complex nonlinear behaviors in multiphase offshore environments. Minimal deviation between predicted and actual critical gas rates was observed, confirming the model's robustness across varying well conditions. The flexible structure of the AI–based model allows seamless integration into digital twin platforms and real–time surveillance systems, enabling dynamic monitoring and proactive management of water loading risks. By adapting to fluctuating operational parameters, the model supports optimization of deliquification strategies, artificial lift planning, and well intervention scheduling. Overall, the AI–driven predictive framework presents a major advancement over conventional methods, offering a reliable, data– driven tool for enhancing gas production stability and extending well life in challenging production environments. Unlike traditional models that primarily focus on droplet or film reversal theories, this study introduces a hybrid AI– based approach that dynamically learns from multiphase behaviors. By integrating entropy-based instability detection and real–time data patterns, the developed model not only predicts the critical rate but also evolves adaptively to changing well conditions, ensuring sustained production optimization.
Cement-casing shear bond strength: a review of the affecting variables and various enhancement techniques Sivakumar Pandian, Gaurav Hazarika, Udita Deota, Divya Shah, Rakesh Kumar Vij Petroleum Science and Technology, 2023 A significant concern in oil and gas well construction is well integrity. The inability to obtain proper zonal isolation during cementation can lead to severe consequences threatening the integrity of the wellbore. Micro-annulus in the bulk cement or behind the casing causes leakage when the cement bonds fail. Thus, the bonding between the cement and the casing is crucial in preserving the wellbore integrity. The bond mechanically supporting the casing pipe in the cement sheath is the cement-casing shear-bond. Many factors have a crucial part in strengthening the shear-bond are discussed in detail. This review systematically summarizes the efforts made on this topic in the recent past. The primary objective of this review is to discuss the factors affecting the strength of the cement-casing shear-bond by describing the different perspectives. It also explains the laboratory and the various bond evaluation methods applied in the field. In addition, it also discusses the challenges and the research gap along with the future scope in this domain.
Artificial Intelligence Enhanced Prediction of Water Loading in Gas Wells: A Data-Driven Alternative to Turner's Model AD Dadwani, G Hazarika ADIPEC , 2025 2025
Screening and feasibility of sustainable Low Saline Enhanced Oil Recovery in a matured sandstone reservoir G Hazarika, RK Vij, A Badoni, H Solanki International Conference on Shaping the Energy Future: Challenges and … , 2025 2025
WellsView360: Optimizing Well Location, Trajectory Analysis, & Forecasting Production M Aastha, J Daksh, H Gaurav International Conference on Oil and Gas for Energy Security-2024 , 2024 2024
Cement-casing shear bond strength: a review of the affecting variables and various enhancement techniques S Pandian, G Hazarika, U Deota, D Shah, RK Vij Petroleum Science and Technology 41 (21), 1971-1984 , 2023 2023 Citations: 11
Hybrid analysis for excessive water production problem G Hazarika, M Das, S Pandian Petroleum Science and Technology 40 (12), 1407-1422 , 2022 2022 Citations: 5
Portable Proppant Transportation Analyzing System PDPU, Dr. Hari S, Dr. Shanker Krishna, Dr. R. K. Vij, Mr. Gaurav Hazarika IN Patent 342065-001 , 2021 2021
Scenario of EOR Screening for Small Fields of Cambay Basin Gaurav Hazarika, Rakesh Kumar Vij 14th India Drilling & Exploration Conference (IDEC-2021), Mumbai , 2021 2021
MOST CITED SCHOLAR PUBLICATIONS
Cement-casing shear bond strength: a review of the affecting variables and various enhancement techniques S Pandian, G Hazarika, U Deota, D Shah, RK Vij Petroleum Science and Technology 41 (21), 1971-1984 , 2023 2023 Citations: 11
Hybrid analysis for excessive water production problem G Hazarika, M Das, S Pandian Petroleum Science and Technology 40 (12), 1407-1422 , 2022 2022 Citations: 5
Artificial Intelligence Enhanced Prediction of Water Loading in Gas Wells: A Data-Driven Alternative to Turner's Model AD Dadwani, G Hazarika ADIPEC , 2025 2025
Screening and feasibility of sustainable Low Saline Enhanced Oil Recovery in a matured sandstone reservoir G Hazarika, RK Vij, A Badoni, H Solanki International Conference on Shaping the Energy Future: Challenges and … , 2025 2025
WellsView360: Optimizing Well Location, Trajectory Analysis, & Forecasting Production M Aastha, J Daksh, H Gaurav International Conference on Oil and Gas for Energy Security-2024 , 2024 2024
Portable Proppant Transportation Analyzing System PDPU, Dr. Hari S, Dr. Shanker Krishna, Dr. R. K. Vij, Mr. Gaurav Hazarika IN Patent 342065-001 , 2021 2021
Scenario of EOR Screening for Small Fields of Cambay Basin Gaurav Hazarika, Rakesh Kumar Vij 14th India Drilling & Exploration Conference (IDEC-2021), Mumbai , 2021 2021
