First EAGE Workshop on High Performance Computing for Upstream in Latin America

Finite difference methods (FDM) are largely used for modeling seismic data with the acoustic wave equation. These methods are computationally intensive, demanding the use of techniques that allow the obtainment of complex results in affordable time. This gives rise to the use parallelization techniques, which can significantly decrease the algorithm execution time. In shared memory environments, the 3D acoustic wave equation might be parallel computed in chunks of data, where the solution is concurrently evaluated, for the different data chunks. The determination of these chunk sizes, also known as workload distribution, is a crucial aspect in this type of approach. In this work we focus in optimize the workload distribution of a 3D FDM algorithm, parallelized with OpenMP, in a shared memory environment. We propose an auto-tuning algorithm that makes use of the global optimization strategy Coupled Simulated Annealing (CSA) to find the chunk size which minimizes the execution time of propagating the seismic acoustic wave equation. We illustrate, in numerical experiments, that the optimal chunk size varies according to the architecture, compiler and number of threads used. We also illustrate, in our tests, that the use of the CSA method is quite promising for the obtainment of the optimal chunk size for these different computational setups, resulting in significant time savings.

The forward and backward modeling steps (necessary to compute the gradient) of the time domain Full waveform Inversion method imply a high computational cost in terms of memory consumption and execution time. In the state-of-the-art exists a computational strategy that consist on reconstruct the backward pressure wavefield from the information at the boundaries of the area of interest. With this method, the used memory could be reduced to less than 5% of the initial value, but the execution time increases approximately 55% due to it is necessary an additional modeling step. A new way to compute the inversion gradient by taking advantage of an inner product property is proposed in this paper. Therefore, if this new gradient calculation is combined with the reconstruction strategy, the extra modeling step is not necessary keeping the same RAM reduction.

Blended (simultaneous) sources in marine seismic acquisitions improve the acquisition efficiency, reducing the acquisition costs and the number of acquired data, obtaining data with mixed information. Traditional data processing for blended data requires a de-blending or separation stage before the reconstruction of velocity model with the FWI method. In this document is developed a source coding optimization in distance and time delay, based on the minimum mutual coherence criteria between blended sources, with the aim to improve the final velocity model avoiding the de-blending step. FWI is implemented to contrast the optimal coded blended distribution with randomly coded blended distribution and traditional equally spaced sources. It is shown that optimal blended distribution of sources improves the final velocity model versus random coding, decreasing the artifacts in the image and obtaining a model closer to the model obtained with traditional acquisition, decreasing the processing time.

In order to explore and exploit natural resources in México, it is required high performance technology and engineering human capacity, with both it is creating methodologies to offer a better quality results. The exploration of natural resources, it is the area of knowledge in which geophysics and geology are practiced, however for these two sciences to be understood in the same space, subsurface images are needed. So, implementing an efficient method to avoid noise signals caused by the same time reverse migration algorithm is the method of absorbing borders. This method proposes to subdivide the seismic image into borders in order to obtain only the propagation and retro-propagation signal without spurious signals. This implementation in the oil industry is necessary to generate greater effectiveness in the seismic inversion and thus the generation of seismic images with a better interpretation. Without this implementation, we have worked in the oil industry for more than 30 years, which is why it is innovative for our oil field projects in Mexico to carry it out. It can be said that when referring to the images in depth we are talking about deep migration PSDM (Pre-Stack Depth Migration) and its elements are input data, pre-processing, migration algorithm and speed model. Reverse-time migration (RTM) is a pre-stacking migration technique that, unlike the rest of migrations, ignores simplifications and uses the full-wave equation [Whitmore, 1983] . In the past decades a great variety of absorbent borders have been developed, especially for the modeling of the seismic wave. Subsequently, among the various attempts to improve the classical PML, (Kuzouglu, 1996) and, (Roden, 2000) developed the convolutional PML or CPML (Convolutional Perfectly Matched Layer) for the Maxwell equations and were adapted to the equations of elastodynamics by Komatitsch and Martin, ( Komatitsch, 2007 ). The latter are used to simulate the direct problem in the present work. The development of the reversal algorithm in time, consisted in the implementation of absorbent borders in the different domains of the seismic image. This implementation considerably improves the seismic image compared to the inverse algorithm without absorbing borders and even with absorbent borders not as elaborate as the development in this thesis work. By means of the implementation of convolutional absorbent borders, it modifies the way of thinking of the current algorithms of programming, for which this thesis work is a clear example of the algorithmic revolution applied to the geophysics of the last decade.

This paper deals with the application of optimization techniques in order to facilitate decision-making regarding the oil reservoir development planning. In this sense, in order to maximize economical profits and incremental oil recovery, it is specified restrictions such as the number and type of new wells to be drilled, the control scheduling as well as optimal locations with the objective to perform a waterflooding project. The method exploits information, which is provided by simulations of a numerical reservoir model to obtain optimal combination of the decision variables. In this study, a modified PSO-MADS algorithm was implemented with capabilities to manage constrains related to existing history wells. Thus, new wells are allowed to be placed only in reservoir avoiding existing well-heads and inactive cells. The effectiveness of the procedure was illustrated by its ability to optimize a complex heavy oil reservoir represented by 2.202.702 grid cells (529.014 active cells, 72 layers) and 11 faults. A total of 420 decision variables were used to solve this problem. The PSO-MADS methodology was adapted, and operational restrictions associated to existing wells were included. In addition to increase the oil recovery, decide the optimal new well placement and control operation as it is shown. The proposed constrained version of PSO-MADS algorithm considers limitations displayed by existing wells. The selected cases showed an optimal location of new wells, resulting in a significant volume of oil recovered and positives values of net present value for reservoir development planning.

Geophysical imaging faces nowadays new challenges related to 3D data acquisition, which means that we need to simulate full 3D volumes. In this work, we present a hybrid MPI/OpenMP approach for modeling the 3D heterogeneous acoustic wave equation to boost the performance of a classical extrapolation scheme. Our solution is a standard eighth order in space and second order in time 3D acoustic FD code, parallelized with MPI/OpenMP and running on Intel general-purpose CPUs. Test cases provided by the High-Performance Computing for Energy project (hpc4e.eu) are solved using our optimized numerical code. We experiment our solution using the flat acoustic tests at 20 Hz maximum frequency. These are codenamed AF-UNIT-20Hz and AF-SURVEY-20Hz. The first test is related to a single shot modeling and the second one simulates an acquisition considering 1681 shots in the subsurface. Results show that a hybrid MPI/OpenMP strategy used to solve the UNIT test in standard multi-core machines is easily scalable to more than one node for the SURVEY experiment. All experiments were run in Lobo Carneiro, a 6000 core machine installed at the High Performance Computing Center at the Federal University of Rio de Janeiro, Brazil.

Seismic Interferometry can be defined from a computationally point of view as the cross-correlation or the deconvolution process between seismic signals, in order to retrieve virtual sources or receivers where only are placed receivers or sources, respectively. This method is mainly used in passive seismics and oil exploration. Depending on the approach, the receivers could be placed over the Earth’s surface or in Vertical Seismic Profiles (VSP). The seismic interferometry becomes an expensive method when is used large seismic data. This because the cross-correlation and the deconvolution between sources is done to each sample in time and space of its receivers. Then, if the seismic data have a large sampling in time and space, this process can become unfeasible with a serial approach algorithm. In this work we will investigate the parallel implementation of the classic seismic interferometric methods based in cross-correlation and deconvolution approaches, in order to find an efficient way to compute them. In the numerical experiments of this work we will consider a 64-bit CPU’s Intel Xeon.

Scale deposition has is a very serious and challenging problem in the oil and gas industry, causing losses of millions of dollars every day (Martinez, 2017) per project. To attack this issue, many engineers have focused on preventing scale formation from the reservoir through squeeze treatments. These treatments involve the injection of a chemical product, which must possess certain key characteristics, in order to effectively prevent scale formation and deposition in the reservoir; but also needs to have a good interaction with the reservoir to attain long squeeze treatments.

The purpose of this paper is to show how those key characteristics that the chemical product needs to have, are represented through mathematical models. The methodology in this paper includes the tests that need to be employed to select the best chemical product, which is selected for an specific reservoir mineralogy, reservoir conditions and physicochemical water composition. Finally, it is going to be shown how to describe this behavior through mathematical models, and how these are employed to reduce and optimize computational costs.