Abstract

SAGD recovery is strongly impacted by distributions of heterogeneous shale barriers, which impede the vertical growth and lateral spread of a steam chamber and potentially reduce oil production. Conventional reservoir heterogeneities characterization workflows that entail updating static reservoir models with dynamic flow data are quite time-consuming. Furthermore, numerical flow simulation could provide only approximate solutions to the recovery responses, as numerous simplifications and assumptions must be invoked. This study proposes a workflow integrating artificial intelligence (AI) in a model selection framework that aims to identify associated shale heterogeneities in SAGD reservoir based on observed decline patterns from production time-series data.

A series of SAGD models based on typical Athabasca oil reservoir properties and operating conditions is constructed. Shale heterogeneities are modeled stochastically by sampling the location, lateral extent and thickness from probabilistic distributions inferred from field data. Each model is subjected to numerical flow simulation and the corresponding production profiles are recorded. First, sensitivity analysis is carried out to identify and parameterize particular patterns observable in the production response that are related to shale characteristics: whenever the steam chamber encounters a shale barrier, a drop in the production is observed; this drop continues until the steam chamber has advanced past the shale barrier, and the production would rise again. The positions and shapes of these decline patterns are retrieved. Next, artificial neural network (ANN) is constructed to calibrate a relationship between the retrieved production pattern parameters (inputs) and the corresponding geologic parameters describing shale heterogeneities (outputs), which include some variables capturing the location, size and orientation of a particular shale barrier encountered by the steam chamber.

Shale barriers located close to the well pair have much more pronounced impacts on the production profiles. Shale barriers that are located far away do not exact a noticeable production pattern. The ANN models are validated using numerous synthetic reservoirs, where the exact shale distributions are known. The trained ANN models can reliably estimate the relevant shale parameters and the associated uncertainties, while accurately predicting the corresponding production responses. It is intended to extend the proposed method to construct the ANN models directly from well logs and production data.

This work presents a preliminary attempt in correlating stochastic shale parameters with observable features in production time-series data using AI techniques. The proposed method facilitates the selection of an ensemble of reservoir models that are consistent with the production history; these models can be subjected to further history matching for a precise final match. The proposed methodology does not intend to replace traditional simulation and history-matching workflows, but it rather offers a complementary tool for extracting additional information from field data and incorporating AI-based models into practical reservoir modeling workflows.

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