In the heart of China’s Ordos Basin, a groundbreaking study is revolutionizing the way the energy sector approaches deep coalbed methane (CBM) production. Led by Wenting Zeng from the College of Petroleum Engineering at the China University of Petroleum (Beijing), this research is tackling a significant challenge: liquid accumulation in horizontal wellbores, a problem that has been stifling the productivity of deep CBM wells.
The Daning-Jixian block, situated on the eastern margin of the Ordos Basin, has seen nearly 150 horizontal wells drilled for CBM extraction. However, as the formation energy depletes, these wells are struggling with declining liquid-carrying capacity, leading to liquid accumulation. This accumulation is a major roadblock, hindering the efficient production of deep CBM.
Zeng and her team have developed a novel approach to diagnose and predict liquid accumulation using advanced computational fluid dynamics. By employing the Reynolds-averaged Navier-Stokes (RANS) κ-ε equation for incompressible viscous fluids and the volume of fluid (VOF) method, they created a numerical model that simulates gas-liquid two-phase flow in horizontal wells. This model was validated using physical simulation experimental results, ensuring its accuracy and reliability.
The study revealed that different flow patterns correspond to varying states of liquid accumulation. Bubble and slug flows indicate liquid accumulation, while churn flow signals a transition state where accumulation is likely to occur. Annular flow, on the other hand, suggests a low risk of liquid accumulation. Moreover, the team found that well inclination is directly proportional to the risk of liquid accumulation, whereas pressure is inversely proportional.
“This research provides a robust method for diagnosing and predicting liquid accumulation in horizontal wells for deep CBM,” Zeng explained. “By understanding the flow patterns and their corresponding risks, we can propose timely intervention and control measures, ultimately improving the efficiency of CBM production.”
The implications of this research are profound for the energy sector. By optimizing the production process and minimizing downtime, energy companies can significantly enhance their operational efficiency and profitability. Furthermore, the study paves the way for future developments in intelligent analysis and prediction using artificial intelligence (AI) techniques.
As Zeng looks to the future, she envisions a seamless integration of AI with the current method. “In subsequent studies, we aim to optimize this method for intelligent analysis and prediction using AI techniques,” she said. “This will provide robust technical support for the prediction, prevention, and control of liquid accumulation in CBM wellbores.”
Published in the journal ‘Meitian dizhi yu kantan’ (translated to English as ‘Petroleum Geology and Engineering’), this study is set to shape the future of deep CBM production. By addressing the critical issue of liquid accumulation, Zeng and her team are not only enhancing the productivity of existing wells but also paving the way for more efficient and profitable CBM extraction in the future.