Combining Regularized Convolutional Neural Networks with Production Data Integration for Geologic Scenario Selection

Abstract

Reservoir model calibration against dynamic response data is often constrained by a prior conceptual model of geologic scenario that specifies the expected types of spatial variability and features in the solution. However, geologists have significant uncertainty in developing a conceptual model, e.g., due to limited data, process-based modeling assumptions, and subjectivity. Therefore, it is prudent to consider the uncertainty in the geologic scenario when solving the model calibration problem as it will provide an opportunity to utilize the response data in supporting or rejecting the proposed scenarios. We present a new approach for geologic scenario identification based on dynamic response data by combining gradient-based inversion for feature extraction and a convolutional neural network for feature recognition. To compactly represent each scenario while ensuring data sensitivity, the approach relies on extremely low-rank parameterization of individual geologic scenarios based on the Principal Components Analysis (PCA). The PCA basis elements of each scenario are then combined to capture the salient features in any of the scenarios, or their possible combinations. An iterative least-squares formulation is then formulated to detect scenarios that are supported by the observed data. The inversion results in an approximate (smooth) spatial solution that contains the dominant spatial patterns. A pre-trained convolutional neural network (CNN) is then used to identify the relevant geologic scenarios based on the reconstructed spatial solution. Two main advantages of the workflow include: (i) the ability to combine different scenarios if supported by data, instead of evaluating individual scenarios, and (ii) efficient gradient-based implementation that does not require extensive forward simulation runs. In addition, the training of CNN is implemented using only geologic realizations without requiring additional reservoir simulation. The performance of the workflow is evaluated using tomographic inversion and model calibration in fluvial reservoirs.

Deep Convolutional Autoencoders for Robust Flow Model Calibration Under Uncertainty in Geologic Continuity

“…Similar learning approaches have also been proposed for complex problems, including the kernel form of PCA or K‐PCA (Sarma et al., 2008) and sparse dictionaries such as K‐SVD (Aharon et al., 2006; Khaninezhad et al., 2012a). Other approaches have also been introduced to deal with situations in which the conceptual geologic model is uncertain, e.g., (Khaninezhad & Jafarpour, 2014; Jiang & Jafarpour, 2019).…”

Convolutional neural networks (CNN) for feature-based model calibration under uncertain geologic scenarios

“…The first class performs scenario falsification before model calibration [6,12,17,37,38,43]. The second class of methods performs scenario falsification after (and in some methods, simultaneously) model calibration [4,10,16,18,20].…”