Fracability Evaluation of Shale of the Wufeng-Longmaxi Formation in the Changning Area, Sichuan Basin

The fracturing technology for shale gas reservoir is the key to the development of shale gas industrialization.It makes much sense to study the mechanical properties and deformation characteristics of shale,due to its close relationship with the fracability of shale gas reservoir.This paper took marine shale in the Changning area,southern Sichuan Basin of China as the research object.Based on field profile and hand specimen observation,we analyzed the development of natural fractures and collected samples from Wufeng Formation and Longmaxi Formation.Combining with the indoor experiment,we investigated the macroscopic and microscopic structural features and the remarkable heterogeneity of shale samples.Then we illustrated the mechanics and deformation characteristics of shale,through uniaxial compression test and direct shear test.The shale has two types of fracture modes,which depend on the angular relation between loading direction and the bedding plane.Besides,the Wufeng shale has a higher value of brittleness index than the Longmaxi shale,which was calculated using two methods,mechanical parameters and mineral composition.Given the above results,we proposed a fracability evaluation model for shale gas reservoir using the analytic hierarchy process.Four influence factors,brittleness index,fracture toughness,natural fractures and cohesive force,are considered.Finally,under the control of normalized value and weight coefficient of each influence factor,the calculations results indicate that the fracability index of the Wufeng Formation is higher than that of the Longmaxi Formation in Changning area,southern Sichuan Basin.

Machine Learning and Data Fusion Approach for Elastic Rock Properties Estimation and Fracturability Evaluation

Accurate rock elastic property determination is vital for effective hydraulic fracturing,particularly Young’s modulus due to its link to rock brittleness.This study integrates interdisciplinary data for better predictions of elastic modulus,combining data mining,experiments,and calibrated synthetics.We used the microstructural insights extracted from rock images for geomechanical facies analysis.Additionally,the petrophysical data and well logs were correlated with shear wave velocity(Vs)and Young’s modulus.We developed a machine-learning workflow to predict Young’s modulus and assess rock fracturability,considering mineral composition,geomechanics,and microstructure.Our findings indicate that artificial neural networks effectively predict Young’s modulus,while K-Means clustering and hierarchical support vector machines excel in identifying rock and geomechanical facies.Utilizing Microscale thin section analysis in conjunction with fracture modeling enhances our understanding of fracture geometries and facilitates fracturability assessment.Notably,fracturability is controlled by specific geomechanical facies during initiation and propagation and influenced by continuity of geomechanical facies in small depth intervals.In conclusion,this study demonstrates data mining and machine learning potential for predicting rock properties and assessing fracturability,aiding hydraulic fracturing design optimization through diverse data and advanced methods.