Bridging the integration gap-simultaneous optimization of well placement, well trajectory, and facility layout

We present an integrated field development planning framework that bridges the integration gap through concurrently optimizing well placement, well trajectory, and facility layout. The novel algorithms implemented in the proposed framework break organizational silos between the reservoir, wells, and facility domains and provide reservoir engineers, drilling engineers, facility engineers, and economists with a shared planning plat-form. The presented solution is modular, flexible, and allows for multiple layers of granularity and, hence, a spectrum of solutions with different trade-offs between accuracy and efficiency needed as the field development plan is refined through its history. Multiple scenarios and example cases are presented illustrating the features of the integrated optimization framework and their applicability in different potential onshore and offshore oil and gas field development projects.A novel machine learning based optimization algorithm for well trajectory design is presented and achieves significant improvements in computational time compared to traditional optimization approaches. Using a machine learning model to design a well trajectory was three orders of magnitude faster than the differential evolution algorithm which, in turn, was the fastest among the different optimization algorithms that we have tested. The proposed machine learning model drastically reduced the CPU requirements of the integrated so-lution and enabled the modeling of complex cases of hundreds of wells and associated facility building blocks.