Parallel Calculation Of Formation Pressure And Parameter Identification In Multi-stage Fracturing Horizontal Wells

Large-scale volumetric fracturing is one of the key technologies for the development of tight oil and gas and shale oil and gas.The evaluation of post-pressurization involves the intersection of mechanics,geology,mathematics and computer science.In this paper,by studying the bottom hole pressure of tight reservoirs and the multi-stage fracturing formation pressure distribution of horizontal wells,a parallel calculation algorithm of formation pressure distribution based on GPU and a new method based on PSO-RBF neural network to identify multi-stage fracturing horizontal well parameters are proposed.Exploring the application of big data in the oil and gas field has important guiding significance for the development and improvement of big data.At the same time,considering the actual tight oil and gas development,the longer the horizontal well,the larger the fracturing scale and the higher the output,but the development cost and water consumption also increase sharply.Through the post-press effect evaluation and capacity prediction,the optimization study of multi-stage fracturing of horizontal wells is carried out,which has important application value for the efficient economic development of tight oil and gas and shale oil and gas.The main research innovations are as follows:1.An unstable seepage model for multi-stage fracturing horizontal wells was established.Through the study of the state equation of tight oil and gas,the continuity equation and the constitutive relation of rock,the seepage equation of multi-stage fractures in horizontal wells is established.The deterministic solution of the seepage model is obtained by the mathematical function analysis methods such as source function theory,three-dimensional eigenvalue method and orthogonal transformation.Finally,through the Laplace transform and the numerical inversion of Stehfest,the bottom hole pressure solution of the multi-stage fracturing well in the horizontal well considering the influence of the reservoir and the skin factor is obtained.2.A parallel computing method for multi-stage fracturing of horizontal wells based on GPU is proposed.In this paper,a mathematical model for multistage hydraulically fractured horizontal wells(MFHWs)in tight oil and gas reservoirs was derived by considering the variations in the permeability and porosity of tight oil and gas reservoirs that depend on formation pressure and mixed fluid properties,and introducing the pseudo-pressure;analytical solutions were presented using the Newman superposition principle.The CPU-GPU asynchronous computing model was designed based on the CUDA platform,and the analytic solution was decomposed into infinite summation and integral forms for parallel computation.Implementation of this algorithm on an Intel i5 4590 CPU and NVIDIA GT 730 GPU demonstrates that computation speed increased by almost 80 times,which meets the requirement for real-time calculation of the formation pressure of MFHWs.3.A new method based on PSO-RBF neural network model is proposed to identify multi-stage fracturing horizontal well parameters.First,the GPU parallel program is used to calculate the bottom-hole pressure of a multi-stage fracturing horizontal well.Second,Most of the above pressure data are imported into the RBF neural network model for training.In the training process,the optimization function of the global optimal solution of the PSO algorithm is employed to optimize the parameters of the RBF neural network,and eventually the required PSO-RBF neural network model is established.Third,the resulting neural network is tested using the remaining data.Finally,a field case of a multi-stage fracturing horizontal well is studied by using the presented PSO-RBF neural network model.The results show that in most cases the proposed model performs better than other models,with the highest correlation coefficient,the lowest mean and absolute error.This proves that the PSO-RBF neural network model can be applied effectively to horizontal well parameter identification.The proposed model has great potential to improve the prediction accuracy of reservoir physical parameters.4.A sampling method based on Latin hypercube is proposed to generate high quality model training samples.Deep machine learning technology has a large demand for the amount of data in training samples.The data samples currently available in oil fields are generally not satisfactory.Combining the field measured data and the interpretation results,the Latin hypercube sampling is used to generate the samples needed for the training of the neural network model.It not only makes up for the shortage of the measured sample size,but also ensures the spatial uniformity of the parameter cluster and greatly improves the prediction accuracy of the PSO-RBF neural network model.5.Data interpretation and analysis of multi-stage fracturing horizontal wells in tight oilfields were carried out using relevant software independently developed by the seepage laboratory,and the results calculated by the software were compared with the predicted results obtained by the PSO-RBF neural network model.The results show that both the analysis software and the PSO-RBF model proposed in this paper can explain the formation parameters quickly and accurately,and give the capacity prediction.