Causal Inference of Interwell Connectivity

Interwell connectivity is a measure of the degree to which information is translated between wells in a reservoir. The translated information may include changes in pressure, fluid flow, or other physical properties. This connectivity is separated into internal connectivities (through the reservoir) and external connectivities (through the infrastructure). Understanding these interwell connectivities may improve the overall understanding of the reservoir, aid in optimizing oil and gas production, and avoid new and undesired connectivities. We seek to determine if causal inference can be applied to aid in the detection of interwell connectivities within the oil and gas field, Eldfisk, operated by ConocoPhillips. Eldfisk is a mature field with a complex network of injection and production wells and a large and connected infrastructure of oil and gas installations. We examined thirteen wells, where eight are water injection wells, and five are oil and gas producing wells. Between these, there are three injection to injection well connectivities and five injection to production well connectivities, for a total of eight well-pair connectivities. These connectivities are known to ConocoPhillips and have been identified through various methods, including manual comparison of pressure responses between wells. The connectivity we seek to identify is a pressure response between a stimulating well and a target well through the reservoir. Data is obtained from Bottom Hole Pressure sensors where this is available and Tubing Head Pressure sensors otherwise. The pressure data is split into smaller datasets that target periods when a stimulating well is experiencing a change in pressure. These periods are identified by an on/off indicator, binary data indicating whether a well is opened or closed. Every well is given status as a stimulating well for each period identified by the on/off-indicator. All other wells are considered as target wells during these periods. Two different causal inference methods were applied to the datasets; Peter Clark Momentary Conditional Independence + (PCMCI+) and Temporal Causal Discovery Framework (TCDF). PCMCI+ is a method that employs partial correlation to infer causality, and TCDF employs convolutional neural networks. We found that the PCMCI+ algorithm was able to identify seven out of eight well-pairs with known connectivities. However, it was less precise, with approximately one out of five identified connectivities being true. The TCDF-algorithm identified three out of eight well-pairs with known connectivities. This model was more precise, with approximately one out of four identified connectivities being true. We also found that the overall complexity of the original dataset was effectively reduced by splitting it into smaller datasets. These datasets specifically targeted periods when the wells experienced changes in their pressure profiles. External interferences from a field-wide water supply were not effectively reduced and were a cause of a majority of false connectivities. Group-wide interference from wells sharing the same production installation was found to be effectively reduced. Using tubing head pressure data when bottom hole pressure data was unavailable, was found to be an effective substitute

Data-Driven Reservoir Modeling using Recurrent Neural Network and Physics-Based Network Model

We present efficient data-driven reservoir model workflows for a mature oil field involving large-scale CO2 Water Alternating Gas (WAG) injection. The CO2 WAG injection is conducted in more than two hundred wells in the entire field, and the operation area is spread throughout the field. Therefore, it is computationally prohibitive to implement history matching or optimization using full-field reservoir models. The objective of this study is to develop efficient data-driven approaches to optimize the CO2 WAG operation and maximize oil recovery from the reservoir. The proposed workflows are useful for predicting future production rates and understanding the reservoir connectivity between producers and injectors. We propose two different types of approaches. First, deep learning algorithms are utilized to develop efficient data-driven reservoir models. Long Short-Term Memory (LSTM) is a special kind of neural network architecture and has been successfully applied to many sequential and time series problems. We formulate time series problems of the production and injection histories, and the LSTM algorithm is used to forecast the future production rate and to estimate the reservoir connectivity. Second, we utilize a physics-based data-driven reservoir model, the 1D network model. The 1D network model characterizes a reservoir by a network grid system, which connects each producer injector pair via a series of 1D grid cells. Numerical reservoir simulators compute the solution of the network grid system. History matching is implemented by Ensemble Smoother with Multiple Data Assimilation (ESMDA), and a streamline-based rate allocation optimization is implemented based on the calibrated network model. The LSTM reservoir modeling workflow was validated using synthetic reservoir cases. It showed reasonable performance on production rates forecasting and reservoir connectivity estimation. Then, we successfully implemented this approach for a real field application. The 1D network model provided suitable history matching results for the entire field application of the mature oil reservoir. Moreover, a streamline-based rate allocation optimization was implemented, and it provided improved oil recovery from the reservoir

LSTM Based EFAST Global Sensitivity Analysis for Interwell Connectivity Evaluation Using Injection and Production Fluctuation Data

In petroleum production system, interwell connectivity evaluation is a significant process to understand reservoir properties comprehensively, determine water injection rate scientifically, and enhance oil recovery effectively for oil and gas field. In this paper, a novel long short-term memory (LSTM) neural network based global sensitivity analysis (GSA) method is proposed to analyse injector-producer relationship. LSTM neural network is employed to build up the mapping relationship between production wells and surrounding injection wells using the massive historical injection and production fluctuation data of a synthetic reservoir model. Next, the extended Fourier amplitude sensitivity test (EFAST) based GSA approach is utilized to evaluate interwell connectivity on the basis of the generated LSTM model. Finally, the presented LSTM based EFAST sensitivity analysis method is applied to a benchmark test and a synthetic reservoir model. Experimental results show that the proposed technique is an efficient method for estimating interwell connectivity