Field practices have demonstrated that three-dimensional well-pad development technology can effectively exploit untapped reserves of shale oil/gas resources. However, the applications of the three-dimensional well-pad development has led to increased horizontal/vertical multi-well interferences. This phenomenon has become increasingly severe, which restricts the efficient development of shale oil and gas resources. Thus, accurately assessing the degree of well interference and its influencing factors is a critical issue that urgently needs to be addressed. To tackle this, we proposed a three-dimensional numerical model of multi-well interference. The proposed model is based on the numerical well-testing model for fractured reservoirs based on unstructured tetrahedral mesh. In addition, the discrete fracture model (DFM) is used to account for non-penetrating geological fracture features. The numerical model is solved using Newton-Raphson iteration. Its accuracy is verified by comparing it with a commercial well testing software. After verification, a coefficient for the degree of well interference is defined, and the analyses of well interference under different well spacings and fracture parameters are conducted. The results indicate that increasing the horizontal spacing leads to a rapid decrease in the degree of well interference. As the vertical spacing increases, the degree of well interference initially increases and then decreases. The influence of horizontal spacing on the degree of well interference is more significant compared to the impact of vertical spacing. The degree of interference increases with the greater production intensity, hydraulic fracture number, hydraulic fracture length, and hydraulic fracture conductivity of adjacent well. The existences of natural fractures can strengthen the degree of well interference. Applying grey relational analysis method, the correlation between the degree of well interference and various influencing parameters was calculated for the well pads from the Ordos Basin. The main controlling factors affecting the degree of well interference are including the horizontal spacing along the fracture direction, fracture length, and fracture number. It is also found that the vertical spacing between wells has a significant impact on the degree of well interference, which should not be underestimated. A reasonable vertical spacing can effectively reduce the degree of well interference. The research results can provide a theoretical basis for the evaluation of well interference and the optimization of development parameters in shale oil development.

Efficient development of shale oil and gas in China relies on factory operations and large-scale fracturing technology. Large-scale fracturing of shale oil and gas requires a long time and numerous equipment and facilities, with frequent and severe incidents of fracturing sand blockage. The research on early warning research in these incidents is crucial for the safety of shale oil and gas fracturing operations. However, the effective methods for analyzing the main control factors of fracturing sand blockage and predicting the pump pressure during operations are lacked. To study this issue, considering the fracturing mechanism and pump pressure variation characteristics, a method for real-time prediction of pump pressure during fracturing operations has been established to conduct sand blockage early warning research here.

First, a fracturing simulator was used to simulate the entire process of pump pressure changes during fracturing. By altering different fluid properties and formation parameters, the main control factors of pump pressure variation were analyzed, and the grey correlation analysis method was used to rank these factors. Secondly, based on fracture mechanics, proppant transport theory, and the Long Short-Term Memory (LSTM) neural network model, a framework and model for predicting pump pressure during operations was established, forming a method for early warning of fracturing sand blockage under the integration of mechanism and intelligence. Finally, the early warning method for sand blockage was applied to actual field fracturing operations.

Results indicate that the factors affecting the pump pressure of a typical well, from most to least significant, are discharge rate, fluid viscosity, differential principal stress, sand concentration, number of fracture clusters, and number of perforations. When other parameters remain constant, as fluid viscosity, differential principal stress, and discharge rate increase, the pump pressure increases; as the number of fracture clusters, perforations, and sand concentration increase, the pump pressure decreases. This method can be used for the identification and early warning of fracturing sand blockage incidents in the actual field operations, which is 19 seconds earlier than on-site manual identification, with a relative error of about 6.8%. The predicted pump pressure is friendly matched with the actual field one, which is helpful in accurate early warning of fracturing sand blockage.

Engineers, geoscientists, and analysts can benefit from fast, easy, and real-time immersive 3D visualization to enhance their understanding and collaboration in a virtual 3D world. However, converting 3D reservoir data formats between different software programs and open-source standards can be challenging due to the complexity of programming and discrepancies in internal data structures. This paper introduces an open-source Python implementation focused on parsing industry reservoir data formats into a popular open-source visualization data format, Visual Toolkit files. Using object-oriented programming, a simple workflow was developed to export corner-point grids to Visual Toolkit-hexahedron structures. To demonstrate the utility of the software, standard raw input files of reservoir models are processed and visualized using Paraview. This tool aims to accelerate the digital transformation of the oil and gas industry in terms of 3D digital content generation and collaboration.