Applied Drilling Engineering Spe Pdf LINK Download

Applied Drilling Engineering Spe Pdf LINK Download

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The main contribution of this paper is to combine the advantages of thePSO optimization algorithm and the LSTM neural network model.We propose an improved LSTM prediction method for ROP prediction.Comparing the LSTM model prediction results before and afteroptimizationshows that the training time of the optimized method is shorterandits accuracy is higher. Our results confirm the feasibility of optimizingthe neural network algorithm through the optimization algorithm, henceintroducing a new approach that can be applied to other aspects ofpetroleum engineering.

The objective of this study was to review the in-situ stress state of anoilfield to predict the ROP and to apply the prediction results in theenhancement of production. The in-situ stress state of an oil field isaffected by the change of reservoir temperature and pressure. Besides,the stress states of the reservoir can be affected by drilling and hydraulicfracturing. The 3D and 2D hydraulic fracture images, drilling logs,and production data of a North Sea oilfield were used as input data. The PSO algorithm was applied to optimize the model parameters of the LSTM neural network. The results showedthat the optimized model prediction was more accurate and could betterreflect the in-situ stress state of the reservoir. This model was of greathelp to the well fracturing project, especially to the evaluation of thepredictive performance of the model. This paper provides a good exampleof applying data-driven modeling technology in the field ofpetroleum engineering.

Second, this paper provides a good example of applying data-drivenmodeling technology in the field of petro engineering. The PSO alsomodel parameter optimization is a good choice for optimizing the LSTMneural network model.

First, this paper reviews the in-situ stress state of an oilfield topredict the ROP and applies the prediction results in the enhancementof production. Although the in-situ stress state of an oil field can beaffected by the change of reservoir temperature and pressure, the stressstates of the reservoir can also be affected by the drilling andhydraulic fracturing. The PSO algorithm is applied to optimize themodel parameters of the LSTM neural network. The results show thatthe optimized model prediction is more accurate and could betterreflect the in-situ stress state of the reservoir.

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