Revolutionizing Reservoir Optimization with Deep Learning Surrogates

In the quest for maximizing output from stress-sensitive unconventional reservoirs, the balance between immediate gains and long-term sustainability poses a formidable challenge. Conventional methods demand a consistent evaluation of intricate flow-geomechanics simulators, resulting in hefty computational expenses.

Innovative Approach

Enter a deep learning-based surrogate optimization framework, poised to transform high-dimensional well control. Unlike traditional techniques that rely on rigid control parameterizations, this novel approach treats the problem as a continuous, high-dimensional puzzle. It's a fresh perspective, employing a sampling strategy informed by real-world trajectories.

How does it work? The method involves training a neural network to mimic the relationship between bottomhole pressure paths and cumulative production, using insights from a coupled flow-geomechanics model. This proxy is then integrated into a constrained optimization process, allowing for swift evaluations of control tactics.

Performance and Precision

The results are compelling. This framework aligns with full-physics solutions within a tight range of 2-5 percent across varied initiations. It achieves this while slashing computational costs by up to three orders of magnitude. That's efficiency unlocked. The main discrepancies arise near the training distribution's edges and due to local optimization nuances.

Why It Matters

Why should the industry care? This framework doesn't just offer a marginal improvement. It redefines what's possible in reservoir management. By reducing costs and enhancing accuracy, it opens new doors for scalable and reliable optimization. The implication is clear: it's not just about managing reservoirs effectively, but doing so with unparalleled efficiency.

The trend is clearer when you see it. Surrogate modeling coupled with problem-informed sampling isn't just a theoretical advancement. It holds promise for a wide range of partial differential equation-constrained systems beyond oil and gas. Could this be the future of resource management across industries?

In an era where data-driven decision-making is key, this approach offers a glimpse into the future. It's a significant step forward, one that could redefine the optimization landscape. Numbers in context: slashing computational costs while maintaining precision is a rare feat. This framework offers both.