A Novel Method for Evaluating Formation Fracturing Effect Utilizing Acoustic Logging

With the development of unconventional reservoirs and continuous advancements in production technology, fracturing has emerged as a crucial method to enhance oil and gas production in tight reservoirs. Unconventional reservoirs typically possess low porosity and permeability, resulting in initially low productivity. Fracturing enables the creation of a fracture network within the reservoir, ultimately improving its permeability and recoverable potential, increasing oil and gas production. However, due to the unique geological characteristics and reservoir conditions of unconventional reservoirs, accurately assessing the effectiveness of fracturing using methods, such as well temperature logging, production fluid profile, and production dynamic, is challenging. Consequently, achieving a precise evaluation of the fracturing effect in unconventional reservoirs remained a technical problem in the exploration of unconventional reservoirs. In the field of acoustic logging, the cross-dipole anisotropy inversion method has proven to be an intuitive and accurate approach for evaluating fracture height. However, the complex fractures geometry and the angle relative to the borehole have caused significant uncertainty in its evaluation, making it inefficient in unconventional applications. Thus, there remains a lack of practical and reliable methods for evaluating the hydraulic fracturing effect in unconventional reservoirs. Based on the dispersion analysis, Slowness Frequency Analysis (SFA), consisting of the projection of the dispersion curve on the slowness axis, can provide information about the formation characteristics such as lithology and fractures. A new method for rapid evaluation of the fracturing effect using array acoustic logging data is proposed. The dispersion analysis method based on linear prediction theory is combined with hierarchical clustering, which considers both the inverted amplitude and slowness during the density-based clustering optimization. With less human intervention and a high degree of automation, high-precision SFA results before and after fracturing are effectively extracted. Compared to traditional methods, the proposed method overcomes challenges such as soft formation and poor data quality after fracturing, enabling reliable extraction of high-resolution dispersion curves within three iterations. By intersecting the projection curve results before and after fracturing, a quantitative and accurate evaluation of the fracturing effect can be achieved. This technology has been successfully applied to the fracturing effect evaluation of shale and sandstone reservoirs in the western basins of China. It has enabled the accurate monitoring of hydraulic fracturing location and the scale of fracturing alteration, providing valuable technical support for evaluating the fracturing effect in unconventional reservoirs.