Production Analysis and Forecasting for Unconventional Reservoirs Using Laplacian Echo-State Networks
Authors: Pan Yuewei , Zhou Peng , Deng Lichi , Lee John 
Published: April 2019

Production data analysis for low permeability shale reservoirs is crucial in characterizing flow regimes and reservoir properties, and the forecasting of production is essential for portfolio and reservoir management. However, traditional methods have failed due to incorrect physics or complicated convolution from the well control history. In this research, we provide a physics-assisted analytics workflow using Laplacian Eigenmaps Coupled Echo-State Network (LEESN) to facilitate and accelerate the analysis of noisy historical production data.

Pressure-rate deconvolution is an ill-posed, complex time-series problem when using the traditional Echo-State Network when the number of training sets is less than the number of neurons. To solve this problem, we apply LEESN to first deconvolve noisy variable-pressure variable-rate histories into smooth constant-pressure rate responses. The physics-based training features and training algorithm provide additional benefits in addition to the analytic approach by honoring transient flow physics. After training, constant-pressure rate responses can be predicted and used for reservoir characterization, as well as production and EUR forecasting through long-term rate predictions to the economic limit.

The proposed workflow was first applied to a synthetic case where the production data were obtained through simulation. The short-term flow rate history was obtained by specifying highly variable controlling pressures. We also added artificial white Gaussian noise to approximate measured signals collected in the field and input this information into LEESN for deconvolution. The constant-pressure rate response was generated after training to determine flow regimes and properties such as permeability using a traditional transient testing specialized plot. All outcomes from the analytics approach were validated by comparison to the input data from the synthetic simulation model. Advantages of the analytics approach were maintained with moderate variation of noisy pressure-rate signals. For production forecasting, both the trained analytics model and simulator were used to predict for an extended time period, and the results indicated good agreement between the response predictions. We performed further sensitivity analysis on important parameters such as the training scale as well as with or without noise in training data. The comparison between the model predictions and simulation data showed significantly increased accuracy in reserves booking and production estimates. Results were further validated from field case studies with actual production data using hindcasting.

This study shows that the LEESN approach is a powerful alternative to interpret pressure-rate-time information from production data. Deconvolved pressure-rate data greatly enhances traditional rate-transient analysis used to characterize reservoirs and enables engineers to predict future production even with noisy, highly-variable production histories. The robustness of the proposed analytics methodology is strengthened by coupling the training features with transient flow physics, and provides a unique approach for production analysis and forecasting for unconventional reservoirs.