In the heart of China’s Hetao Basin, a groundbreaking method is revolutionizing the way energy companies approach complex reservoirs, offering a beacon of hope for more efficient oil extraction. The Xinglong Structural Belt, known for its challenging reservoir conditions, has long posed difficulties for accurate classification and evaluation. However, a new study published in the journal ‘Cejing jishu’ (translated to ‘Petroleum Geology and Engineering’) is changing the game.
The Linhe formation in this region is characterized by low geothermal temperatures and deep burial, leading to weak diagenetic features and strong heterogeneity. Traditional methods of reservoir classification, which rely heavily on pore and permeability parameters, have struggled to keep up with the increasing depth of exploration. This has often resulted in layers with similar physical properties yielding vastly different production outcomes, a frustrating reality for energy professionals.
Enter LI Xiaofeng, a researcher from the Logging Technology Research Institute at China National Logging Corporation in Beijing. LI and his team have developed a novel approach that combines nuclear magnetic resonance (NMR) logging with conventional logging data to create a more precise evaluation method. “The key lies in understanding the pore structure,” LI explains. “By integrating NMR data, we can gain insights that traditional methods miss.”
The team’s method involves constructing several key parameters, including the geometric mean of NMR T2 spectra, different pore ratios, conventional porosity, permeability, and mud content. These parameters form the backbone of their reservoir classification evaluation method, which is centered around nuclear magnetic pore structure evaluation. But they didn’t stop there. To address the issue of low accuracy in areas lacking NMR logging, LI and his colleagues turned to machine learning algorithms and multi-parameter fusion. This innovative approach has significantly improved the accuracy of reservoir classification, providing energy companies with a powerful tool for decision-making.
The implications for the energy sector are substantial. Accurate reservoir classification can lead to more targeted drilling, reduced costs, and increased oil recovery. As LI puts it, “This method is not just about improving accuracy; it’s about guiding the industry towards more efficient and effective exploration strategies.”
The application of this method in the Linhe formation of the Xinglong Structural Belt has already shown promising results, effectively guiding oil layer testing and selection. As the energy industry continues to push the boundaries of exploration, methods like these will be crucial in navigating the complexities of deep and challenging reservoirs.
The research published in ‘Cejing jishu’ marks a significant step forward in reservoir evaluation technology. As energy companies strive for more efficient and sustainable practices, innovations like LI Xiaofeng’s method will undoubtedly play a pivotal role in shaping the future of the industry. The integration of advanced technologies and data-driven approaches is not just a trend; it’s becoming a necessity in the ever-evolving energy landscape.