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ABSTRACT Currently, it is challenging to determine the geological parameters of natural caves in deep reservoirs accurately. By inheriting the advantages of Machine Learning (ML) method and physics modelling, a novel ML-Physics method is developed to determine the geological parameters of natural caves based on the data obtained during Hydraulic Fracturing (HF) operation. The process of ML-Physics method is divided into preparation-stage and operation-stage. The preparation-stage happens before HF operation without the limitation of computational time, during which the implicit relationship between cave property and fracturing curve is generated. The computational time of the operation-stage is limited because the geological parameters of natural caves should be determined in real time during HF operation. During the operation-stage, the physical modelling based inverse analysis method is carried out, in which the initial value is chosen based on the results of preparation-stage. Results show that, with the same target error, the iteration step required by ML-Physics method is much less than that of traditional inverse analysis method. With the same iteration steps, the error of ML-Physics method is lower than that of the traditional inverse analysis method. The ML-Physics method is potentially useful to optimize the HF design in real time. INTRODUCTION The exploitation of oil and gas resources is very important for maintaining world energy security and keeping economic development. It is necessary to determine the geometry and geological parameters of natural caves to optimize the hydraulic fracturing design (Ma et al., 2018; Ali et al., 2019). Currently, seismic tomography is widely used to detect the distribution of natural fractures and caves (Chalikakis et al., 2011; Xu et al., 2016). With the rapid development of Internet of Things (IoT) technology, large quantities of monitoring data can be obtained in real time (Wang et al., 2022). Therefore, it is critical to develop an available method to determine the geological parameters according to the evolution of fracturing curves.
- Research Report > New Finding (0.48)
- Research Report > Experimental Study (0.34)
- Geology > Rock Type > Sedimentary Rock (1.00)
- Geology > Geological Subdiscipline (0.72)
Abstract We studied the initiation and propagation of mode II fractures in granite and sandstone under confining pressure to investigate the controls on shear fracture propagation in rocks. An asymmetric loading set up was used to induce a fracture in cylindrical rock samples under confining pressure between 0-20 MPa. We achieved quasi-static fracture propagation with a refined AE feedback displacement control. This technique prolongs the fracturing process up to 42 hours, provides a higher AE resolution and thereby allowed the distinction of two different stages in shear fracture propagation. Granitic samples form vertical fractures in the strain strengthening stage that branch and stop propagating at peak stress. Simultaneously at peak stress a distinct diagonal fracture nucleates on the loaded side of the vertical fracture. During strain weakening we observed stable growth of this second diagonal fracture until the sample lost its integrity. On the other hand sandstone samples only form the diagonal fracture during the strain weakening stage. Analysis of AE source type and hypocenter as well as microstructural analysis indicate that porosity, either intrinsic (sandstone) or deformation inflicted (granite), primarily influences this fracture nucleation and propagation behavior for both rock types. 1. INTRODUCTION Fractures form as microscopic cracks coalesce into a planar structure, which can be captured by monitoring acoustic emissions (AEs). Macroscopically, rocks will fracture in the mode (I or II) corresponding to the mode of loading determined by the orientation of the fracture relative to the stress state. However at a microscopic scale, both modes of fracturing can be found in what may appear to be a pure mode of fracturing at the macroscopic scale [1, 2]. According to the acoustic emissions observed during rock fracturing experiments, shear-, tensile-, and compression-events all occur during macroscopic mode II fracture propagation [3]. On a microscopic scale on the other hand, increased confining pressure suppresses the occurrence of mode I fractures and supports the occurrence of mode II fractures [4]. These observations demonstrate the complexity of rock fracturing on a microscopic scale and thereby raised a controversy about the validity of macroscopic fracture modes. The existence of a fracture criterion for mode II is still debated in the literature [5, 6]. To study this, many authors used acoustic emissions to stabilize the fracture process [1, 7, 8, 9, 10]. By controlling the load in response to the intensity of the observed AEs, the rate of fracturing was varied and fracture propagation could be visualized in detail.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Igneous Rock > Granite (0.90)
Numerical Investigation of the Interaction Between Hydraulic Fractures and Nature Fractures at Reservoir Scale
Zhu, Guolong (Khalifa University of Science and Technology) | Sousa, Rita (Khalifa University of Science and Technology) | Abdulla, M. B. (Khalifa University of Science and Technology) | Abu Al-Rub, R. (Khalifa University of Science and Technology) | Sassi, M. (Khalifa University of Science and Technology)
ABSTRACT: Hydraulic fracturing is a proven means of enhancing rock mass permeability in tight reservoirs, which ultimately leads to a more efficient and increased productivity. However, the existence of natural discontinuities, may cause propagation of hydraulic fractures to be arrested or offset. In this paper, simulations of hydraulic fracturing within a reservoir containing one natural discontinuity are conducted to evaluate the behavior of hydraulic fractures when approaching a natural discontinuity. This is done using the discrete element modeling method. A series of simulations were performed to determine the effect of different factors such as the far field stresses, the angle of approach, the intersection distance and the mechanical properties of the discontinuities. The results of the simulations will provide a better understanding of the hydraulic fracture behaviors in the reservoir and will serve as basis for more complex reservoir studies and for the development of a hydraulic fracturing module to be compatible with an existing natural fracture network model.
- Asia > Middle East > UAE (0.29)
- North America > United States (0.28)
Unconventional Reservoir Management Modeling Coupling Diffusive Zone/Phase Field Fracture Modeling and Fracture Probability Maps
Wheeler, Mary F. (The University of Texas at Austin, USA) | Srinivasan, Sanjay (Pennsylvania State University, USA) | Lee, Sanghyun (Florida State University, USA) | Singh, Manik (Pennsylvania State University, USA)
Abstract Optimal design of hydraulic fractures is controlled by the distribution of natural fractures in the reservoir. Due to sparse information, there is uncertainty associated with the prediction of the natural fracture system. Our objective here is to: i) Quantify uncertainty associated with prediction of natural fractures using micro-seismic data and a Bayesian model selection approach, and ii) Use fracture probability maps to implement a finite element phase-field approach for modeling interactions of propagating fractures with natural fractures. The proposed approach employs state-of-the-art numerical modeling of natural and hydraulic fractures using a diffusive adaptive finite element phase-field approach. The diffusive phase field is defined using the probability map describing the uncertainty in the spatial distribution of natural fractures. That probability map is computed using a model selection procedure that utilizes a suite of prior models for the natural fracture network and a fast proxy to quickly evaluate the forward seismic response corresponding to slip events along fractures. Employing indicator functions, diffusive fracture networks are generated utilizing an accurate computational adaptive mesh scheme based on a posteriori error estimators. The coupled algorithm was validated with existing benchmark problems which include prototype computations with fracture propagation and reservoir flows in a highly heterogeneous reservoir with natural fractures. Implementation of a algorithm for computing fracture probability map based on synthetic micro-seismic data mimicking a Fort Worth basin data set reveals consistency between the interpreted fracture sets and those observed in the reference. Convergence of iterative solvers and numerical efficiencies of the methods were tested against different examples including field-scale problems. Results reveal that the interpretation of uncertainty pertaining to the presence of fractures and utilizing that uncertainty within the phase field approach to simulate the interactions between induced and natural fracture yields complex structures that include fracture branching, fracture hooking etc. The novelty of this work lies in the efficient integration of the phase-field fracture propagation models to diffusive natural fracture networks with stochastic representation of uncertainty associated with the prediction of natural fractures in a reservoir. The presented method enables practicing engineers to design hydraulic fracturing treatment accounting for the uncertainty associated with the location and spatial variations in natural fractures. Together with efficient parallel implementation, our approach allows for cost-efficient approach to optimizing production processes in the field.
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.55)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.48)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.48)
Study on Diagnosis Model of Shale Gas Fracture Network Fracturing Operation Pressure Curve
Lin, Ran (Southwest Petroleum University) | Wang, Zhenhua (Southwest Petroleum University) | Ren, Lan (Southwest Petroleum University) | Zhao, Jinzhou (Southwest Petroleum University) | Jiang, Tingxue (Sinopec Research Institute of Petroleum Engineering)
Abstract In recent years, shale gas development has expanded rapidly. For a shale reservoir with low porosity and ultra-low permeability, multi-cluster fracturing with a high pump rate, large fracturing fluid volume and a low fluid viscosity is the key technology to increasing shale gas production. Due to the strong heterogeneity, relatively developed weak planes such as natural fractures and bedding fractures, and the large fracturing fluid leak-off, hydraulic fractures usually propagate non-uniformly and non-continuously and form into complex fractures network, making the pressure curve of shale gas fracturing much more complex than that of the conventional fracturing. Consequently, the conventional pressure curve diagnosis approach cannot be applied to shale gas fracturing. This paper established six criteria for different propagation patterns of fractures network and proposed an adaptive diagnostic model for shale gas fracturing pressure curves. Then, the diagnostic model was applied to the typical fracturing wells, and the fracturing curves were analyzed through bottomhole net pressure conversion, dynamic segmentation, fracture propagation pattern recognition and fracture network complexity evaluation. Finally, the paper discussed the positive correlation between fracture network complexity evaluated by our diagnostic model and the fracture network complexity interpreted by microseismic monitoring. This research provides a practical method to optimize the shale gas fracturing design and operation, and enhance the potential of shale reservoir development.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Asia > China > Sichuan > Sichuan Basin (0.99)
- Asia > China > Qinshui Basin (0.99)