Study on detection methods of abnormal structural planes in shale reservoirs in the Hongxing area

Abnormal structural planes in shale reservoirs are divided into hard planes and weak planes. The former is typically represented by limestone interbeds, and the latter includes bedding fractures and natural fractures. Their development characteristics are closely related to reservoir accumulation, hydraulic fracturing, and oil and gas production efficiency. Accurate identification of the types and development intervals of these planes is of great practical significance for the exploration and development of shale oil and gas. Taking shale reservoirs in the Hongxing area as the research target, this study aims to propose a set of effective logging-based detection methods for different types of abnormal structural planes, so as to address the difficult classification and low identification accuracy of these planes in continental shale reservoirs.

Using the sensitivity of array sonic logging and electrical imaging logging to the lithology and structural characteristics of shale reservoirs, a combined logging detection method based on array sonic logging and electrical imaging logging was adopted for the identification of abnormal structural planes. Firstly, the energy difference between fast and slow shear waves from array sonic logging was used to accurately identify the limestone interbeds and other hard planes. Secondly, the Gini coefficient of electrical imaging logging was introduced, and the development characteristics of bedding fractures were quantitatively characterized by calculating the degree of fluctuation of the Gini coefficient. Finally, combined with the array sonic correlation coefficient and other logging parameters, as well as the dark strips in the acoustic waveform logs, the qualitative identification and quantitative characterization of natural fractures were realized. In the research process, the acoustic energy values and attenuation coefficients were calculated using Fourier transforms and the strain tensor using the computational Fourier transform moiré (STC) method. The variance of the Gini coefficient was used to quantify the degree of fluctuation, and the pearson correlation coefficient of the frequency spectra of fast and slow shear waves was defined as the core parameter for identifying natural fractures.

The research results showed that the energy difference effectively responded to the hard planes of limestone interbeds in the Hongxing area, clearly reflecting their development intervals. The Gini coefficient method addressed the heavy workload and poor performance in traditional single-strip identification of bedding fractures, enabling efficient and quantitative characterization of fracture development. The combination of the abnormally low values of the array sonic correlation coefficient and the dark strips in waveform logs provided an intuitive representation of natural fracture development, allowing visual determination of their positions and scales. Field testing in Well Hong A showed clear differentiation of logging responses for each type of abnormal structural plane, and the identification results were highly consistent with core and lithofacies column data.

Field verification shows that this method enables accurate and rapid identification of the development intervals of limestone interbeds, bedding fractures, and natural fractures in vertical wells. For horizontal wells, due to the lack of electrical imaging logging data, the bedding fracture development intervals cannot be identified, but limestone interbeds and natural fractures can still be quantitatively detected using array sonic logging parameters and waveform logs. Verification with lost circulation data from the horizontal section of Well Hong A confirmed consistency with fracture identification results, demonstrating the method’s feasibility and reliability in horizontal wells. The proposed logging detection method enriches the diversity of identification methods for abnormal structural planes in shale reservoirs and provides a reliable technical reference for precise layer selection and optimized fracturing design in the Hongxing area and similar continental shale reservoirs.