By Sina Mohajeri, Matthias Hartung - Target Energy Solutions

Reservoir models are vitally important in the oil and gas industry, for example for estimating remaining oil and gas reserves and providing production forecasts. Managing the operational and financial performance of an E&P asset is also heavily dependent upon having access to a current and reliable prediction of the dynamic reservoir behaviour.

All this information is crucial for creating dev and operational plans and providing an audi cash 'ow to share - holders. It is therefore imp have an up-to-date and and accurate reservoir model.

This means that frequent model updates are simply unaffordable. Engineers therefore often resort to using simpler approximation models to forecast production and remaining reserves, as these methods are faster and cheaper to use, although at the price of losing prediction accuracy.

However, E&P asset operators have an issue, in that their optimum history matched dynamic reservoir models are becoming quickly out-of-date and ‘dormant’, due to the length of time it takes to update models.

This industry dilemma led engineers and scientists of the international technology and services company Target Energy Solutions to develop a new hybrid modelling method that couples conventional simulation with modern machine learning, thereby combining engineering knowledge of subsurface data with machine learning acquired through the stream of ‘big data’ coming from production.

A Revolutionary Breakthrough

This hybrid AI/physics-based system uses a conventional 3D, 3-phase numerical reservoir simulator coupled with a complex deep learning engine. The first step is to simply integrate an asset’s optimum dynamic reservoir model into Target’s platform MEERA, and its AI-Simulation framework therein. This is done by re-assembling the components of the original conventional model, so it meshes into the machine learning framework. Various parameters are extracted from the original reservoir model, some of which, based on a comprehensive correlation analysis, are selected as key reservoir elements to be used for machine learning training and prediction. It takes just minutes to train the AI using historic production data, while successive prediction simulations take merely milliseconds. The lack of time constraints means that it is possible to optimise a development strategy by running numerous scenarios in minutes, all based on the experience of the asset’s petroleum engineers.

New production data is then incorporated, creating a rapidly self-updating live dynamic reservoir model that can be used as a reliable production forecasting and scenario-testing tool for operational planning and budgeting.

In comparison to purely data-driven processes, which mainly capture near-well behaviours, the system, known as MEERA Simulation, considers the whole reservoir’s interconnected behaviour and therefore all inter-well effects. This makes it ideal for studying the effects of processes like well injection, as well as for looking at how major structures like fractures and faults in the reservoir may impact the production rates. A reservoir model that is constantly being updated has the major advantage of allowing reliable maps of remaining hydrocarbons to be made, which can then be used to select optimum infill drilling locations. It also helps to provide stable and auditable cash-'ow predictions to shareholders.

KeyFacts Energy Industry Directory: TARGET