Artyom Semenikhin

Reducing Data Movement Costs: Scalable Seismic Imaging on Blue Gene

We present an optimized Blue Gene/P implementation of Reverse Time Migration, a seismic imaging algorithm widely used in the petroleum industry today. Our implementation is novel in that it uses large communication bandwidth and low latency to convert an embarrassingly parallel problem into one that can be efficiently solved using massive domain partitioning. The success of this seemingly counterintuitive approach is the result of several key aspects of the imaging problem, including very regular and local communication patterns, balanced compute and communication requirements, scratch data handling, multiple-pass approaches, and most importantly, the fact that partitioning the problem allows each sub-problem to fit in cache, dramatically increasing locality and bandwidth and reducing latency. This approach can be easily extended to next-generation imaging algorithms currently being developed. In this paper we present details of our implementation, including application-scaling results on Blue Gene/P.

Efficient brownfield optimization of a reservoir in west Siberia

In this work we present a methodology for optimal management of brownfields that is illustrated on a real field. The approach does not depend on the particular reservoir flow simulator used, although streamline-derived information is leveraged to accelerate the optimization. The method allows one to include (non-linear) constraints (e.g. a recovery factor larger than a given baseline value), which are very often challenging to address with optimization tools. We rely on derivative-free optimization coupled with the filter method for non-linear constraints, although the methodology can also be combined with approaches that utilize exact/approximate gradients. Performance in terms of wall-clock time can be improved further if distributed-computing resources are available (the method is amenable to parallel implementation). The methodology is showcased using a real field in west Siberia where net present value (NPV) is maximized subject to a constraint for the recovery factor. The optimization variables represent a discrete time series for well bottom-hole pressure over a fraction of the production time frame. An increase in NPV of 7.9% is obtained with respect to an existing baseline. The optimization methods studied include local optimization algorithms (e.g. generalized pattern search) and global search procedures (e.g. particle swarm optimization). The controls for one injection well in the real field were actually modified according to the solution determined in this work. The results obtained suggest improvement for most economic scenarios.

Efficient Brownfield Optimization of a Reservoir in West Siberia

In this work we present a methodology for optimal management of brownfields that is illustrated on a real field. The approach does not depend on the particular reservoir flow simulator used although streamline-derived information is leveraged to accelerate the optimization. The method allows one to include (nonlinear) constraints (e.g., recovery factor larger than a given baseline value), which are very often challenging to address with optimization tools. We rely on robust (derivative-free) optimization combined with the filter method for nonlinear constraints. It should be noted that the approach yields not only a feasible optimized solution but also a set of alternative infeasible solutions that could be considered in case the constraints can be relaxed. The whole procedure is accelerated using streamline-derived information. Performance in terms of wall-clock time can be improved further if distributed-computing resources are available (the method is amenable to parallel implementation). The methodology is showcased using a real field in West Siberia where net present value (NPV) is maximized subject to a constraint for the recovery factor (RF). The optimization variables represent a discrete time series for well bottomhole pressure over a fraction of the production time frame. An increase in NPV of 7.9% is obtained with respect to an existing baseline. The optimization methods studied include local optimization algorithms (e.g., Generalized Pattern Search) and global search procedures (e.g., Particle Swarm Optimization). We provide solutions with different levels of approximation and computational efficiency. Without the acceleration achieved through streamline-derived information, the method, while effective, could be prohibitive in many practical scenarios. It is worthwhile noting that part of the solution determined in this work has been tested out on the real field. Optimal management of brownfields is typically addressed using bottomhole pressure values or rates as well control variables. Well controls given as bottomhole pressure values, although not directly implementable in the real field, are often much easier to put into practice than if they are given as rates. However, optimization algorithms that deal with well rates as control variables can be in many cases computationally faster than methods based on bottomhole pressure values. In this work we combine the two aforementioned desirable features for the optimal management of mature fields: well controls are given as bottomhole pressure values for a more practical implementation, and these values are also determined efficiently using concepts borrowed from optimization via well rates.