Engineers, geoscientists, and analysts can benefit from fast, easy, and real-time immersive 3D visualization to enhance their understanding and collaboration in a virtual 3D world. However, converting 3D reservoir data formats between different software programs and open-source standards can be challenging due to the complexity of programming and discrepancies in internal data structures. This paper introduces an open-source Python implementation focused on parsing industry reservoir data formats into a popular opensource visualization data format, Visual Toolkit files. Using object-oriented programming, a simple workflow was developed to export corner-point grids to Visual Toolkit-hexahedron structures. To demonstrate the utility of the software, standard raw input files of reservoir models are processed and visualized using Paraview. This tool aims to accelerate the digital transformation of the oil and gas industry in terms of 3D digital content generation and collaboration.Document Type: Short communicationCited as: Tosta, M., Oliveira, G. P., Wang, B., Chen, Z., Liao, Q. APyCE: A Python module for parsing and visualizing 3D reservoir digital twin models. Advances in Geo-Energy Research, 2023, 8(3): 206-210. https://doi.org/10.46690/ager.2023.06.0

Chen, Zhiming

Liao, Qinzhuo

Oliveira, Gustavo P.

Tosta, Mateus

Wang, Bin

English

Yandy Scientific Press

Advances inGeo-Energy Research Vol. 8, No. 3, p. 206-210, 2023Short communicationAPyCE: A Python module for parsing and visualizing 3Dreservoir digital twin modelsMateus Tosta1, Gustavo P. Oliveira1, Bin Wang2 *, Zhiming Chen2, Qinzhuo Liao21TRIL Lab, Department of Scientific Computing, Federal University of Paraı́ba, João Pessoa 58000-000, Brazil2National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum, Beijing 102249, P. R. ChinaKeywords:Reservoir modelinggrid processing3D visualizationdigital twinCited as:Tosta, M., Oliveira, G. P., Wang, B.,Chen, Z., Liao, Q. APyCE: A Pythonmodule for parsing and visualizing 3Dreservoir digital twin models. Advances inGeo-Energy Research, 2023, 8(3):206-210.https://doi.org/10.46690/ager.2023.06.07Abstract:Engineers, geoscientists, and analysts can benefit from fast, easy, and real-time immersive3D visualization to enhance their understanding and collaboration in a virtual 3D world.However, converting 3D reservoir data formats between different software programs andopen-source standards can be challenging due to the complexity of programming anddiscrepancies in internal data structures. This paper introduces an open-source Pythonimplementation focused on parsing industry reservoir data formats into a popular open-source visualization data format, Visual Toolkit files. Using object-oriented programming,a simple workflow was developed to export corner-point grids to Visual Toolkit-hexahedronstructures. To demonstrate the utility of the software, standard raw input files of reservoirmodels are processed and visualized using Paraview. This tool aims to accelerate the digitaltransformation of the oil and gas industry in terms of 3D digital content generation andcollaboration.1. IntroductionData visualization resources are indispensable for anysoftware intended to handle 3D models of oil and gas (O&G)reservoirs. As never seen before, the groundbreaking powerof computer graphics and scientific visualization have broughtendless capabilities for geoscientists, engineers, and analyststo understand subsurface phenomena. Multidimensional viewsgreatly improve seismic interpretation, reservoir characteriza-tion, and real-time monitoring of producing fields, since high-resolution images and 3D models can reconstitute the physicalworld almost perfectly (Masison et al., 2021).Despite the rapid spread of new paradigms that head upthe O&G digital transformation, such as digital twins, artificialintelligence, and machine learning, numerical simulators arestill distinguished assets for reproducing the multiphase dy-namics of reservoir fluids, performing history matching, andoptimizing well placement strategies (Sircar et al., 2022; Sunand Zhang, 2020).Many simulators with popularity among the O&G com-munity use discrete formulations based on corner-point grids(Ponting, 1989). Corner-point grids can be considered an inter-national standard to represent all the geologic features found incomplex reservoirs, including dips, bends, pinches, and faults.While many tools for post-processing of corner-point gridsare available, most of them are independent and have theirown internal semantic and object hierarchy, which preventstheir full integration. Furthermore, because the interoperabilityof grid files (in terms of importing/exporting data) amongsuch programs depends on complex programming routines,the representation of modeling entities and features from agiven software may be mismatched when read into another.As seen, data integration and data visualization are perma-nent challenges for software and algorithm developers workingon solutions for the O&G sector. In particular, researchersoften need visualization tools to communicate their discov-eries, make reports with interactive plots, and easily generatepublication-ready graphics of post-processed data.∗Corresponding author.E-mail address: metzkerrr@gmail.com (M. Tosta); gustavo.oliveira@ci.ufpb.br (G. P. Oliveira); bin.wang@cup.edu.cn (B. Wang);zhimingchn@cup.edu.cn (Z. Chen); liaoqz@cup.edu.cn (Q. Liao).2207-9963 © The Author(s) 2023.Received May 21, 2023; revised June 3, 2023; accepted June 9, 2023; available online June 12, 2023.Tosta, L., et al. Advances in Geo-Energy Research, 2023, 8(3): 206-210 207Fig. 1. Several models of reservoirs are shown and analyzed using APyCE.A considerable number of commercial and open-sourcesoftware capable to deal with corner-point grids for visualiza-tion purposes are known. Either they have embedded viewersas secondary functionality or are primarily tailored for visual-ization. In the first group, CMG Results© (https://www.cmgl.ca/results), Schlumberger Petrel© (https://www.software.slb.com/products/petrel), ESSS Kraken© (https://oilandgas.esss.co/technologies/kraken), and Amarile RE-Studio© (https://www.amarile.com/-RE-Studio-.html); in the second group,SINTEF’s MRST (Lie, 2019) and Ceetron Solution’s ResIn-sight (https://resinsight.org) are a few options. Minor open-source software projects have been proposed by scholarsand independent scientists with an interest in O&G data,but they are usually focused on flow simulations instead ofvisualization and corner-point grid manipulation.This paper introduces a lightweight Python-based packagefor the plotting of reservoir models discretized through Carte-sian or corner-point grids called APyCE. The objective of thissoftware is to provide a simple pipeline for post-processingthrough the PyVista module (Sullivan and Kaszynski, 2019)and Visual Toolkit (VTK) exporting for 3D visualization insideKitware Paraview software (Ayachit et al., 2015).APyCE is an enhanced version of the early project Py-GRDECL (Wang, 2018), developed to handle SchlumbergerEclipse© (Schlumberger, 2014) deck files for visualization.APyCE is multi-platform and easy to use, being prepared tofulfill its objectives with only 4 lines of code and requiringno background in Python to be run. It is recommended forresearchers who need to render high-quality figures for in-clusion into scientific papers, reports, presentations, handouts,interactive notebooks, and general documents for teachingpurposes.Next, the computational framework is discussed in Section2, examples of use are provided in Section 3, and lastly, someconcluding remarks and perspectives for future developmentare drawn.2. BackgroundAPyCe is developed on top of Eclipse deck files’ structure(Schlumberger, 2014). However, one should stress that similartopologies are found in most of the input files managed bycompeting software. Such files are internally divided intosections. Each section admits a broad set of keywords thatreflect the model’s complexity. The higher is the number ofkeywords to be processed, the greater is the level of details onstratigraphy, fluid properties, well control, numerical settings,and so on.In particular, Eclipse’s more general files that containgrid specifications have a .GRDECL extension. Initially, weimplemented a parser routine that recognizes only the essentialkeywords to build either a corner-point or block-centered grid(Ponting, 1989). All keywords of the file are identified by aregular expression like ˆ[A-Z][A-Z0-9]{0,7} (Fig. 2).2.1 Corner-point gridsCorner-point is a grid format for 3D geometries thatenables us to describe reservoirs with geological realism.Sometimes it is referred to as “pillar grid” because of en-sembles of pillars that span from the top to the bottom ofthe model. The grid cells are then defined by eight pairednodes encountered over four adjacent pillars (Fig. 3). Sincethe nodes are free to slide along the pillars, several features,such as pinch-outs, and folds are accurately and consistentlyrepresented, even with degenerated or nonconform cells.2.2 Block-centered gridsBlock-centered grids are Cartesian-like domains specifiedby multidirectional step sizes and top reference coordinates208 Tosta, L., et al. Advances in Geo-Energy Research, 2023, 8(3): 206-210SPECGRID20 20 4 1 F /COORD0.60814319E+03 -0.12195820E+04 0.22497217E+04 0.59895313E+03 -0.12417069E+04 0.24990918E+040.65048248E+03 -0.12046404E+04 0.22456648E+04 0.64349963E+03 -0.12271340E+04 0.24927664E+040.69211285E+03 -0.11901328E+04 0.22466001E+04 0.68799969E+03 -0.12132166E+04 0.24919016E+040.73225159E+03 -0.11736407E+04 0.22484019E+04 0.73173944E+03 -0.11976670E+04 0.24935242E+040.78713696E+03 -0.11509646E+04 0.22498828E+04 0.79193066E+03 -0.11765302E+04 0.24963970E+04...Fig. 2. Excerpt of a .GRDECL file for a reservoir model highlighting the SPECGRID and COORD keywords. The ellipsisare not part of the syntax and here they only illustrate continuity of the file content.Fig. 3. Simple illustration of adjacent cells in a typical corner-point grid underpinned by arbitrary pillars. Adapted from (Lie,2019).(keywords DX, DY, DZ, and TOPS). In this type of grid, thecells are sugar cuboids that arrange to approximate a regularthree-dimensional volumes, i.e., “shoebox” models (Fig. 4).3. Methodology3.1 File processingThe processing of the file, where the topology and ge-ometry of the grid will be calculated, takes place in theprocess_grid function. For each of the NX ∗ NY ∗ NZcells present in the mesh, the algorithm will retrieve the rawcorner-point data from the keywords COORD and ZCORN or,it will retrieve the raw block-centered geometry data from thekeywords DX, DY, DZ and TOPS.For corner-point geometries, X and Y values are interpo-lated from the ZCORN. It is assumed by APyCE that allpillars are straight lines, making linear interpolation sufficient.In special cases where collapsed pillars occur, meaning pillarsof the form (x0 y0 z0 : x0 y0 z0) where the top point of the pillarcoincides with the bottom point of the pillar, cells followingthe ZCORN convention must be recovered. In the case ofblock-centered geometry, VTK points and cells are createdmanually. The tool’s workflow is illustrated in Fig. 4. Theminimum code required to run the tool is to specify the file toprocess and its origin (Eclipse/Builder functions to get a VTUfile).After creating the VTK points, the software will retrievethe cells that follow the VTK pattern, as this pattern is differentfrom the one followed by the Eclipse software. Fig. 5 showsthe VTK pattern for a hexahedron object. Eclipse softwareuses different indexing than VTK uses. For the software inquestion, points 2 and 3 in Fig. 5 are ”swapped” in positionFig. 4. Simple illustration of a block-centered Cartesian grid.with each other; the same goes for points 6 and 7. Whencarrying out these changes, a standard method in VTK isadopted, simply invoking the export_data function toexport the file for later viewing in the Paraview software.Fig. 5. Orientation of the points of a hexahedron within theVTK, the value 12 represents the hexahedron within the VTK(Schroeder et al., 2002).3.2 Keyword handlingThe current version of APyCE supports the followingkeywords.• SPECGRID: specifies the number of cells in each direc-tion of a corner-point grid.• DIMENS: ditto for block-centered grids.• COORD: specifies the coordinates (X,Y,Z) of the pillars.• ZCORN: indicates the depth of each cell corner over thepillars.• PORO: stores the porosity values per cell.• PERMX: stores the X-direction permeability for each gridcell.• PERMY: ditto for the Y -direction.• PERMZ: ditto for the Z-direction.• INCLUDE: appends supplementary files to the main file.• ÅCTNUM: tags grid cells as active/inactive (booleanvalue).• DX: specifies the step size over the X-direction for block-centered grids.• DY: ditto Y -direction.• DZ: ditto Z-direction.• TOPS: stores the reference value from the top for eachcell in a block-centered grid.Tosta, L., et al. Advances in Geo-Energy Research, 2023, 8(3): 206-210 209Block-centered?StartEclipse deckfile inputRead fileGridtypeCorner-point?Create VTKPoints and cellsCreate VTKPoints and cellsHandledegenerate cellsCreate VTKPoints and cellsExportVTK/ VTUEnd# vtk DataFile Version x.yvtk outputASCIIDATASET UNSTRUCTURED_GRIDPOINTS n float(...) VTK file templateRUNSPECGRIDEDITPROPSREGIONSSOLUTIONSUMMARYSCHEDULEGrid geometry and basic rock properties1 import apyce as ap23 G = ap.grid.Grid(filename=‘Data/PSY.grdecl’,grid_origin=‘eclipse’,verbose=True)4 G.process_grid()5 G.export_data()GRDECL file(keywords overview)Basic code instructionsFig. 5. Workflow of APyCE.3.3 Code frameworkThe Python programming language is used because itis interpreted, object oriented, high-level, and with dynamicsemantics. In addition, the choice for the Python languagefor the development of the project is justified by the vastamount of modules available to perform scientific computing,signal processing, and graphic manipulation tasks, such asNumPy, SciPy, and Matplotlib, in addition to having a libraryfor the use of VTK-The Visualization Toolkit, an open-source toolkit for 3D computer graphics, image processing,and visualization, created and maintained by Kitware, Inc.Another point that strengthens the choice for the Python lan-guage is its simplicity, which reduces software maintenance.Through the available modules and packages, modularizedprogramming and code reuse is encouraged. The support forprogramming in an object-oriented paradigm is another factorthat contributes to the choice of the Python language, sinceeach object, or class, can be compared to real-world elementsand defined based on these elements. Finally, reading andmaintaining extensive code developed in an object-orientedparadigm becomes extremely simple when compared to otherprogramming paradigms (functional, imperative, declarative,structural, etc.).4. Usage and developmentOn the ApyCe repository, one can finds the example scriptGetting_Started.py, which reproduces the basic codeinstructions as depicted at the top of the flowchart (Fig. 5)and generates multiple VTK files.Lastly, the outputs can be visually improved in Paraviewfrom a plethora of applicable filters (Fig. 1). The tool iscapable of rendering several models of reservoirs with differenttopologies, even those comprising faults or complex stratigra-phy.Regarding software, the following points are noteworthy:APyCE is written in Python 3.8. The choice for thislanguage is justified by the vast amount of modules avail-able to perform scientific computing, signal processing, andgraphic manipulation tasks. Another point that strengthens thechoice for Python is its simplicity, which reduces softwaremaintenance. Through the available modules and packages,modularized programming and code reuse is encouraged.Processing highly refined grids (hundreds or millions ofcells) may demand increased RAM and processor power. Yetless complex grids are manageable affordably.To work properly, APyCE requires the following Pythonpackages: NumPy (v. 1.19.2 or above), VTK (v. 8.2.0 orabove), PyVista (v. 0.29 or above), and Matplotlib (v. 3.4.1or above). The most current version was tested on Ubuntu20.04 distribution with the 5.11.0-25-generic kernel anw with210 Tosta, L., et al. Advances in Geo-Energy Research, 2023, 8(3): 206-210PyTest (https://docs.pytest.org/en/latest/). All the workflow forexporting/visualizing corner-point grids are fully workable, butblock-centered grids still have partial keyword support.5. ConclusionIn this paper, we presented a Python module tailored forviewing 3D reservoir models with varying structural complex-ity. When parsing the input files, the module’s routines areable to recognize the main features of corner-point and block-centered grids.Beyond its open-source nature, easy manipulation andintegration with Paraview software are other strong points ofthis module. One the one hand, it allows that any researcherwith minimal knowledge of programming can run the code. Onthe other hand, it provides high-quality outputs readily usablefor general data analysis or publication purposes.This module can help all professionals working at theoil and gas upstream, mainly reservoir engineers, geologists,and modelers who need to understand spatial distributions oranalyze field-scale properties. From this point on, efforts willbe made to extend the module’s capabilities to parse otherfile formats, support additional model features, and providesoftware interchangeability. Code profiling tests in highlyrefined meshes are needed to verify processing efficiency andbottlenecks.AcknowledgementsG.P.O and M.M.T acknowledge the National Council forScientific and Technological Development (CNPq-Brazil) forthe financial support granted through PIBIC/UFPB Program2020-2021 and CT-PETRO Program (No. 405654/2022-7).B.W acknowledge the Natural Science Foundation of China(No. 52204021), Science Foundation of China University ofPetroleum, Beijing (No. 2462022BJRC002) for the financialsupport.Conflict of interestThe authors declare no competing interest.Code availabilityAPyCE can be downloaded directly from its project pageon https://github.com/mateustosta/apyce-project.Open Access This article is distributed under the terms and conditions ofthe Creative Commons Attribution (CC BY-NC-ND) license, which permitsunrestricted use, distribution, and reproduction in any medium, provided theoriginal work is properly cited.ReferencesAyachit, U., Geveci, B., Avila, L. The Paraview Guide: AParallel Visualization Application. Kitware, New York,USA, 2015.Lie, K. A. An introduction to reservoir simulation usingMATLAB/GNU Octave: User guide for the MATLABReservoir Simulation Toolbox (MRST). Cambridge, UK,Cambridge University Press, 2019.Masison, J., Beezley, J., Mei, Y. et al. A modular computa-tional framework for medical digital twins. Proceedingsof the National Academy of Sciences of the United Statesof America, 2021, 118(20): e2024287118.Ponting, D. K. Corner point geometry in reservoir simula-tion. European Association of Geoscientists & Engineers,1989: cp-234.Schlumberger. Eclipse Reservoir Simulation Software Refer-ence Manual. Schlumberger, Texas, USA, 2014.Schroeder, W., Martin, K. M., Lorensen, W. E. The Visu-alization Toolkit An Object-Oriented Approach to 3DGraphics. Prentice-Hall, New Jersey, USA, 1998.Sircar, A., Nair, A., Bist, N. et al. Digital twin in hydro-carbon industry. Petroleum Research, 2022, in press,https://doi.org/10.1016/j.ptlrs.2022.04.001.Sullivan, C., Kaszynski, A. Pyvista: 3d plotting and mesh anal-ysis through a streamlined interface for the visualizationtoolkit (vtk). Journal of Open Source Software, 2019,4(37): 1450.Sun, S., Zhang, T. A 6m digital twin for modeling andsimulation in subsurface reservoirs. Advances in Geo-Energy Research, 2020, 4(4): 349-351.Wang, B. Pygrdecl a python-based grdecl visualization library,2018.

APyCE: A Python module for parsing and visualizing 3D reservoir digital twin models

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APyCE: A Python module for parsing and visualizing 3D reservoir digital twin models

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