Application of Data Analytic Techniques and Monte-Carlo Simulation for Forecasting and Optimizing Oil Production from Tight Reservoirs

Abstract

Prediction of well production from unconventional reservoirs is a complex problem even with considerable amounts of data especially due to uncertainties and incomplete understanding of physics. Data analytic techniques (DAT) with machine learning algorithms are an effective approach to enhance solution reliability for robust forward recovery forecasting from unconventional resources. However, there are still some difficulties in selecting and building the best DAT models, and in using them effectively for decision making. The objective of this study is to explore the application of DAT and Monte-Carlo simulation for forecasting and enhancing oil production of a horizontal well that has been hydraulically fractured in a tight reservoir. To do this, a database was first generated from 495 simulations of a tight oil reservoir, where the oil production in the first year depends on 16 variables, including reservoir characteristics and well design parameters. Afterward, using the random forest algorithm, the most influential parameters were determined. Considering the optimum hyperparameters for each algorithm, the best algorithm, which was identified through a comparative study, was then integrated with Monte-Carlo simulation to determine the quality of the production well. The results showed that oil production was mainly affected by well length, reservoir permeability, and number of fracture stages. The results also indicated that a neural network model with two hidden layers performed better than the other algorithms in predicting oil production (lower mean absolute error and standard deviation). Finally, the probabilistic analysis revealed that the completion design parameters were within the appropriate range.