Stone Ridge Technology (United States)

Abstract In subsurface rocks, the brine can act as a carrier where non-hydrocarbon components, either natively present or introduced externally, are transported and possibly react with each other as well as with the porous medium. The proposed paper describes the integration of PHREEQC, a leading open-source software for 0D/1D geochemical modeling, with a commercial GPU-based reservoir simulator. This will provide the reservoir engineering community with a user-friendly tool incorporating reactive transport into reservoir studies. PHREEQC is widely used for batch geochemical modeling, thanks to its proven robustness as well as compatibility with extensive databases. We present a non-intrusive approach to enabling reactive transport in reservoir simulation by sequentially coupling a GPU-based reservoir simulator with PHREEQC. The former solves reservoir equations and the transport of chemical master species as aqueous tracers, and the latter computes solutes and rock composition at the end of each time step, based on the master species content in each cell. Then porosity, absolute and relative permeability are explicitly updated by the reservoir simulator as a function of the geochemical state. In this work, two application examples of reactive transport modeling (RTM) using the proposed approach are shown; first RTM is used to simulate in a mechanistic manner a low-salinity EOR process, and second to study injection impairment due to scale deposition. Note that chemical reactions of hydrocarbon/light components dissolved in the aqueous phase are not supported in the current implementation. First, results include an assessment of the method accuracy, as well as a discussion of practical advantages compared to alternative coupling approaches; because all relevant chemical reactions information is kept within PHREEQC datafiles, seamless integration between reservoir and geochemical studies is ensured. Second, results include a set of performance benchmarks to assess the computational cost of such explicit coupling, in particular considering that while the simulator runs on GPUs, chemical reactions are computed on CPUs. This paper shows the first steps towards the integration of RTM capabilities in a commercial GPU reservoir simulator, with the objective of developing the ability to account for advanced chemical processes required for both hydrocarbon recovery and energy transition problems, minimizing the impact on simulator infrastructure and leveraging capabilities of state-of-the-art CPU-based geochemical codes.

Abstract Dicamba-resistant soybean was developed and commercialized by Monsanto in 2016, and in recent years, barnyardgrass has become more troublesome for growers who use residual herbicides with dicamba technology. Field studies were conducted from 2019 to 2021 in Stoneville, Mississippi, to evaluate barnyardgrass control after applications of glyphosate or glyphosate + dicamba, when mixed with residual herbicides, and when applied sequentially. In the first field study, glyphosate (1,120 g ae ha −1 ) and glyphosate + dicamba (560 g ae ha −1 ) were applied in combination with common residual herbicides. The second field study included an initial treatment with glyphosate (1,120 g ha −1 ), glyphosate + dicamba (560 g ha −1 ), and glyphosate + dicamba + S -metolachlor (1,064 g ai ha −1 ) followed by a sequential treatment of glyphosate or glyphosate + dicamba at 3 and 7 d after an initial herbicide treatment. Results indicated that glyphosate alone provided greater barnyardgrass control than glyphosate + dicamba. Additionally, at 28 d after treatment, pyroxasulfone, pyroxasulfone + fluthiacet, dimethenamid-P, and S -metolachlor did not affect postemergence control of barnyardgrass after glyphosate + dicamba treatments. Furthermore, sequential herbicide treatments of glyphosate or glyphosate + dicamba led to no difference in barnyardgrass control 28 d after the sequential treatment. These results indicate that options exist for adding residual herbicides to glyphosate + dicamba treatments and that sequential treatments of glyphosate or glyphosate + dicamba are important for optimizing barnyardgrass control.

SummaryRecently, graphics processing units (GPUs) have been demonstrated to provide a significant performance benefit for black-oil reservoir simulation, as well as flash calculations that serve an important role in compositional simulation. A comprehensive approach to compositional simulation based on GPUs has yet to emerge, and the question remains as to whether the benefits observed in black-oil simulation persist with a more complex fluid description. We present a positive answer to this question through the extension of a commercial GPU-based black-oil simulator to include a compositional description based on standard cubic equations of state (EOSs). We describe the motivations for the selected nonlinear formulation, including the choice of primary variables and iteration scheme, and support for both fully implicit methods (FIMs) and adaptive implicit methods (AIMs). We then present performance results on an example sector model and simplified synthetic case designed to allow a detailed examination of runtime and memory scaling with respect to the number of hydrocarbon components and model size, as well as the number of processors. We finally show results from two complex asset models (synthetic and real) and examine performance scaling with respect to GPU generation, demonstrating that performance correlates strongly with GPU memory bandwidth.NOTE: This paper is also published as part of the 2021 SPE Reservoir Simulation Conference Special Issue.

It is an undisputed truth that demand for computational performance for simulating very large models in upstream applications is ever increasing. This demand can be met conceptually in one of two ways. “Scale-out”, implies exploiting additional computational nodes, while “scale-up” implies increasing the computational power, particularly floating point throughput and memory bandwidth of each node. In practice, these two approaches provide opposite bounds on a spectrum of cluster designs, from the use of many relatively weak, “thin” nodes, to a smaller number of powerful, “fat” nodes. The scale-out approach gained increasing dominance in HPC as scalability was prefered over absolute efficiency. Over the past decade, however, energy efficiency has become the key performance limiter. For applications with significant communication requirements, including reservoir simulation, the use of scale-up fat nodes provides an opportunity to localize communications and minimize interconnect traffic, thereby increasing energy efficiency. However, harnessing fat fat nodes comprising of several extremely high-performance GPUs to achieve performance for implicit simulations requires careful software design and novel algorithmic approaches. We will first present the algorithmic and computational challenges faced and the approaches needed to efficiently utilize the massive parallelism offered by such scaled-up nodes.

The Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code is part of the Department of Defense High Performance Computing Modernization Program (HPCMP) technology insertion (TI) benchmarking suite of applications. As a component of the TI benchmarking applications, LAMMPS is a significant contributor to Computational Chemistry and Materials Science requirements within the DoD. Ensuring its optimal performance on HPCMP resources is a high priority. The CCM-KY5-003 User Productivity Enhancements and Technology Transfer (PET) project was created to profile and optimize LAMMPS to improve its performance and efficiency on two HPCMP assets, Eagle (Altix 3700) and JVN(Linux Cluster). Profiling efforts were completed on Eagle using the Tuning and Analysis Utilities (TAU) application in conjunction with the Performance Application Programming Interface (PAPI). The time and hardware counter data was analyzed with ParaProf and PerfExplorer, while the trace data was automatically analyzed by the Kit for Objective Judgment and Knowledge-based Detection of Performance Bottlenecks (KOJAK). The profiling effort used three different model systems, where each uses a different physical potential. Several opportunities for performance improvement were identified in the critical portions of the code, such as loop-static branching conditions, multi-dimensional C-arrays and redundant integer, and floating-point operations. In addition to algorithmic changes, compiler options and machine-specific instructions were also investigated. The performance of the code increased from 1.3x to 3.5x on Eagle and 1.2x to 1.8x on JVN depending on the physical potential employed