Exploration of hydrocarbon reservoirs, particularly those characterized as low seismic amplitude gas fields, poses considerable challenges for geologists and energy companies. A recent study published on January 29, 2025, presents groundbreaking work conducted at the West Delta Deep Marine (WDDM) concession off the coast of Alexandria, Egypt, utilizing machine learning techniques to revolutionize hydrocarbon exploration.
The WDDM concession, covering 1850 square kilometers of the northwestern Nile Delta, has long been recognized for the potential hydrocarbon resources residing beneath its seafloors. According to estimates from the US Geological Survey (USGS), the region holds 1.8 billion barrels of recoverable oil and 223 trillion cubic feet of recoverable gas. Despite this promising outlook, traditional seismic interpretation methods have often struggled to depict subtle seismic expressions typically associated with low amplitude gas fields.
This study introduces a comprehensive workflow aimed at detecting low seismic amplitude gas sands, which are often overlooked by conventional exploration methods due to their non-distinct seismic signatures. The researchers employed seismic spectral decomposition and Amplitude Versus Offset (AVO) analysis, integrating machine learning algorithms to isolate and accurately classify target anomalies.
The seamless integration of these advanced techniques enables geologists to map subtle variations hidden within seismic data, thereby enhancing the ability to spot profitable hydrocarbon reservoirs. The study articulates, “This integrated approach reduces exploration risk, quantifies the chance of success, and enhances decision-making in well placement and hydrocarbon exploration.”
Spectral decomposition techniques were utilized to delineate non-channelized low seismic amplitude gas sands found within the WDDM concession, paving the way for subsequent AVO classifications. By investigating how amplitude varies with offset, researchers were able to confirm gas sand anomalies, improve fluid classifications, and provide insights applicable to future drilling endeavors.
Crucially, machine learning models showcased their efficacy by detecting low seismic anomalies by leveraging attributes extracted from seismic datasets. This added precision allows for more reliable identification of hydrocarbon prospects, addressing the perennial concerns over drilling non-productive wells. The researchers noted, “Machine learning techniques offer significant advantages… improving the reliability of prospect identification.”
This study’s iterative approach included rigorous testing on blind sections—areas intentionally excluded from the training dataset—to validate the results against known geological parameters. The successful identification of the low-amplitude Swan-E Messinian anomaly, previously regarded as high-risk due to its subtle signature, serves as evidence of this proposed workflow’s effectiveness.
With its findings showing good agreement with existing geological models, the study presents strong evidence supporting the use of machine learning for delineation and classification of gas fields, as echoed by the authors who stated, “The results showed good agreement with the known geology, confirming the effectiveness of the workflow in identifying potential hydrocarbon reservoirs.”
Through advanced methodologies, including spectral decomposition and machine learning, this research sets the stage for future explorations within complex geological settings, promising to not only improve the prospects of success for energy companies but also enhancing the accuracy of reservoir characterization. Notably, the researchers concluded: “The workflow developed… provides a systematic approach to reduce exploration risk and improve the accuracy of reservoir characterization.”
Consequently, as the hydrocarbon market increasingly emphasizes the need for innovative and effective detection techniques, studies such as this pave the way for the next generation of exploration methodologies, offering pragmatic solutions for future drilling projects.