Abstract

A physics-assisted deep learning model is presented to facilitate transfer learning in unconventional reservoirs by integrating the complementary strengths of physics-based and data-driven predictive models. The developed model uses a deep learning architecture to map formation properties to their corresponding production responses using a low-dimensional feature space representation. Transfer learning is accomplished by first training the network weights using production data from a mature shale play and combining the learned weights with limited data from a new unconventional field to generate a predictive model. The simulated data provides approximate production predictions for input parameters of the target field, for which the source data may not provide a good prediction. The resulting model has a superior performance to simulation-based and data-driven predictions alone. The results indicate that (1) physics-based simulated data can facilitate production predictions when out-of-range (unseen) input parameters have to extrapolate from data, and (2) transferring the weights learned from the source field to the target field can add valuable information to enhance the prediction performance of the target field.

Introduction

There has been a significant increase in the development of unconventional reservoirs in recent decades, particularly tight oil reservoirs in the United States. These tight oil formations often have extremely low permeability and are not economically exploitable using traditional drilling and completion techniques used for conventional high permeability reservoirs. Fortunately, due to the advancement in key technologies, in particular hydraulic fracturing and horizontal drilling, these low permeability shale formations can be stimulated to induce the production of hydrocarbons (King, 2010). However, the traditional flow equations that are well documented and studied for conventional wells have not been applied to tight oil reservoirs.

Traditionally, the development of conventional reservoirs relies heavily on numerical reservoir simulators (Altman et al., 2020, Aziz et al., 1979). These simulators provide a reliable prediction that assists in decision-making for resource development. They are built using well-studied and trusted physics-based analytical expressions that are solved during the simulation to provide prediction responses. Since these physics-based simulation models are based on analytical equations, they have the advantage of being able to produce a prediction response for any possible range of input variables, and this allows them to easily extrapolate to any range of data. Additionally, simulation models can be run for any combination of input variables and enable the collection of large amounts of simulated data for all possible scenarios. However, the equations used in simulation models are not able to provide dependable predictions for unconventional tight oil resources since the complex physical relation of flow from tight formations and fractures along with the fracture generation is poorly understood. Additionally, there are various components in the field that are not always accounted for when building simulation models (Fung et al., 2016). These limitations make simulation models unreliable to be used to develop unconventional reservoirs.

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