Modeled and empirical approaches to evaluate rock petrophysics parameters: a case study of Dhodak Gas Field, Pakistan

• Autorzy: Khan Sarfraz , Ullah Shuja , Nisar Umair B. , Ahmed Waqas , Mughal Muhammad Rizwan , Irfan , Nawaz Shahid , Rehman Nazir Ur

Identyfikatory

DOI

10.18668/NG.2024.09.01

Warianty tytułu

PL

Modelowe i empiryczne podejście do oceny parametrów petrofizycznych skał na przykładzie złoża gazu Dhodak w Pakistanie

• Języki publikacji: EN

Abstrakty

EN

The main aim of the presented study was to perform an analysis of petrophysical rock parameters for the Pab Formation, which is a potential reservoir, located at four-depth intervals within the Dhodak Gas Field. The Pab Formation in the studied wells is mostly composed of the well-sorted sandstone. The intervals for the study were selected by applying a gamma ray log cut-off of 40%. A Matlab code that is user-friendly and contains empirical and numerical models (Raymor’s, Han's, Castagna’s, Jizba, Blangy, and Gardner's models) was designed with the purpose of providing an interpretation of rock physics parameters in reservoirs. The results indicate that the Pab Formation is mainly saturated with hydrocarbons and possesses sufficient porosity (i.e. between 8 and 38%). According to the cross-plot models, there are a few zones within the Pab Formation that are weakly consolidated. These zones are located at depths of 2377–2404 m and 2418–2430 m. However, the same area is classified as tight gas sandstone and is located between 2330 to 2343 m. The results of the study indicate that the Pab Formation is potentially saturated with hydrocarbons. These findings were based on structural and rock physics assessments of the study area. The approach presented in this work is also helpful for structural trap evaluation. In addition, the petrophysical rock parameter models developed can contribute to reducing the risks associated with hydrocarbon exploration, enabling accurate representation of subsurface conditions and improving reservoir forecasting capabilities in oil producing areas.

PL

Głównym celem prezentowanych badań było przeprowadzenie analizy parametrów fizycznych skał dla serii piaskowcowej Pab, która jest potencjalną skałą zbiornikową, zalegającą w czterech interwałach głębokościowych w obrębie złoża gazu Dhodak. Formacja Pab w analizowanych odwiertach zbudowana jest głównie z dobrze wysortowanych piaskowców. Interwały do badań zostały wybrane poprzez zastosowanie odcięcia na profilowaniu gamma na poziomie 40%. Kod Matlab, który jest przyjazny dla użytkownika i zawiera modele empiryczne i numeryczne (modele Raymora, Hana, Castagny, Jizby, Blangy’ego i Gardnera), został zaprojektowany w celu umożliwienia interpretacji parametrów petrofizycznych skał. Wyniki wskazują, że piaskowiec Pab jest głównie nasycony węglowodorami i ma zadowalającą porowatość (tj. pomiędzy 8 a 38%). Na podstawie analizy wykresów krzyżowych stwierdzono, że istnieje kilka stref w obrębie formacji Pab, które są słabo skonsolidowane. Strefy te lokalizowane zostały w głębokościach 2377–2404 m i 2418–2430 m. Jednak w tym samym rejonie występuje piaskowiec gazonośny (o charakterze tight gas) znajdujący się na głębokości 2330–2343 m. Wyniki przeprowadzonych badań wskazują, że piaskowiec Pab może być potencjalnie nasycony węglowodorami. Ustalenia te oparto na ocenie strukturalnej oraz ocenie parametrów petrofizycznych skał w obszarze badań. Podejście przedstawione w tej pracy jest również pomocne do oceny pułapek strukturalnych. Ponadto opracowane modele parametrów petrofizycznych skał mogą się przyczyniać do zmniejszenia ryzyka związanego z poszukiwaniami węglowodorów, umożliwiając dokładne odwzorowanie warunków panujących pod powierzchnią i poprawiając możliwości prognozowania złóż w obszarach wydobycia ropy naftowej.

Słowa kluczowe

EN

rock petrophysics parameters; empirical models; MATLAB coding; crossplots; Middle Indus basin

PL

parametry petrofizyczne skał; modele empiryczne; kodowanie MATLAB; crossploty; basen środkowego Indusu

• Wydawca: Instytut Nafty i Gazu - Państwowy Instytut Badawczy
• Czasopismo: Nafta-Gaz
• Rocznik: 2024
• Tom: R. 80, nr 9
• Strony: 531--550
• Opis fizyczny: Bibliogr. 33 poz., rys.

Twórcy

autor

Khan Sarfraz

• National Centre of Excellence in Geology, University of Peshawar

autor

Ullah Shuja

• National Centre of Excellence in Geology, University of Peshawar

autor

Nisar Umair B.

• Centre for Climate Research and Development, COMSATS University Islamabad

autor

Ahmed Waqas

• National Centre of Excellence in Geology, University of Peshawar

autor

Mughal Muhammad Rizwan

• Department of Meteorology, COMSATS University Islamabad

autor

Irfan

• National Centre of Excellence in Geology, University of Peshawar

autor

Nawaz Shahid

• National Centre of Excellence in Geology, University of Peshawar

autor

Rehman Nazir Ur

• Department of Geology, Khushal Khan Khattak University Karak

Bibliografia

• Agbauduta E.A., 2014. Evaluation of in-fill well placement and optimization using experimental design and genetic algorithm. Master’s Thesis Report, Petroleum Engineering Department, African University of Science and Technology, Nigeria.
• Amit K., Zhiling G., 2012. Applied fuzzy logic approach to intergraded qualitative survey data into Traditional MCDM. Hawaii International Conference on System Sciences.
• Avseth P., Mukerji T., Mavko G., Dvorkin J., 2010. Rock-physics diagnostics of depositional texture, diagenetic alterations, and reservoir heterogeneity in high porosity siliclastic sediments and rocks-A review of selected models and suggested workflows. Geophysics, 75(5): 75A31–75A47. DOI: 10.1190/1.3483770.
• Bhaskar B., Bhadoria R.S., Yadav A.S., 2013. Transportation system of coal distribution: A fuzzy logic approach using MATLAB. Corona Journal of Science and Technology, 2(3): 20–30.
• Blangy J.P., 1992. Integrated Seismic Lithologic Interpretation: The Petrophysical Basis. Ph.D. Dissertation, Stanford University.
• Boah E.A., Kondo O.K.S., Borsah A.A., Brantson E., 2019. Critical evaluation of infill well placement and optimization of well spacing using the particle swarm algorithm. Journal of Petroleum Exploration and Production Technology, 9(4): 3113–3133. DOI:10.1007/s13202-019-0710-1.
• Cao C., Gu X., Xin Z., 2010. Stochastic chance constrained mixed-integer nonlinear programming models and the solution approaches for refinery short-term crude oil scheduling problem. Applied Mathematical Modeling, 34(11): 3231–3243. DOI: 10.1016/j.apm.2010.02.015.
• Castagna J.P., Batzle M.L., Eastwood R.L., 1985. Relationship between compressional wave and shear wave velocities in clastic silicate rocks. Geophysics, 50(4): 571–581. DOI: 10.1190/1.1441933.
• Castagna J.P., Batzle M.L., Kan T.K., 1994. Offset-dependent reflectivity – Theory and practice of AVO analysis. [In:] Castagna J.P., Backus M.M. (Eds.). Rock physics – the link between rock properties and AVO response. Investigations in Geophysics, 8 135–171.
• da Veiga L.B., Lipnikov K., Manzini G., 2014. The Mimetic Finite Difference Method for Elliptic Problems, volume 11 of MS&A – Modeling, Simulation and Applications. Springer. DOI:10.1007/978-3-319-02663-3
• Draege A., Johansen T.A., Brevik I., Draege C.T., 2006. A strategy for modeling the diagenetic evolution of seismic properties in sandstones. Petroleum Geosciences, 12(4): 309–323.
• Emujakporue G., Nwankwo C., Nowasu L., 2012. Integration of well logs and seismic data for prospect evaluation of an X-field, onshore Niger Delta, Nigeria. International Journal of Geosciences,3(24): 872–877. DOI: 10.4236/ijg.2012.324088.
• Forth S.A., 2006. An efficient overloaded implementation of forward mode automatic differentiation in MATLAB. ACM Trans Math Software, 32(2): 195–222. DOI: 10.1145/1141885.1141888.
• Gardner G.H.F., Gardner L.W., Gregory A.R., 1974. Formation velocity and density: the diagnostic basics for stratigraphic traps. Geophysics, 39(6): 770–780.
• Han D., 1986. Effects of porosity and clay content on acoustic properties of sandstones and unconsolidated sediments. Ph.D. Dissertation, Stanford University.
• Humayon M., Akram M., Malik M.R., 2012. Dhodak Field: A case history of first and largest condensate discovery of Pakistan. AAPG Search and Discovery, 20147.
• Jizba D.L., 1991. Mechanical and Acoustic Propoerties of sandstone and shales, Volume 45 of Standford rock physics and borehole project. Department of Geophysics, School of Earth Sciences publishers, Standford University.
• Kearey P., Brooks M., Hill I., 2002. An Introduction to Geophysical Exploration. Blackwell Science Ltd., Oxford.
• Khan S., Hanif M., Ali S., Nisar B.U., Latif K., Jan I.U., Pasha A.R., 2015. Lithological interpretation of middle Miocene Gaj Formation, Indus Offshore, Pakistan. Journal of Himalayan Earth Sciences, 48(2): 108–116.
• Lie K.-A., 2016. An Introduction to Reservoir Simulation Using MATLAB User Guide for the MATLAB. Reservoir Simulation Toolbox (MRST). SINTEF ICT, Department of Applied Mathematics, Oslo, Norway.
• Mahmood M.F., Shakir U., Abuzar M.K., Khan M.A., Khattak N.M., Hussain H.S., Tahir A.R., 2017. Probabilistic neural network approach for porosity prediction in Balkassar area: A case study. Journal of Himalayan Earth Sciences, 50(1A): 111–120.
• Margrave G., Lamoureux M., 2019. Numerical Methods of Exploration Seismology: With Algorithms in MATLAB. Cambridge University Press. DOI: 10.1017/9781316756041.
• Mavko G., Mukerji T., Dvorkin J., 2009. The Rock Physics Handbook, Tools for Seismic Analysis of Porous Media. Cambridge University Press. DOI: 10.1017/CBO9780511626753.
• Moghal M.A., Saqi M.I., Hameed A., Bugti M.N., 2007. Subsurface Geometry of Potwar Sub-basin in relation to Structuration and Entrapment. Pakistan Journal of Hydrocarbon Research, 17:61–72.
• Moore H., 2009. MATLAB for Engineers. Prentice Hall.
• Naseer M., Khoso T., Alam M.S., Amin S., Saqib M., 2007. Formation Pressure Prediction Using Seismic Inversion Technique in Central Indus Basin, Pakistan. Proceedings of the ATC Conference, Islamabad, Pakistan, 85–88.
• Nazeer A., Solangi S.H., Brohi I.A., Usmani P., Napar L.D., Jahangir M., Hameed S., Ali S.M., 2013. Hydrocarbon Potential of Zindapir Anticline, Eastern Sulaiman Fold Belt, Middle Indus Basin, Pakistan. Pakistan Journal of Hydrocarbon Research, 22(23):73–84.
• Okwu M.O., Nwachukwu A.N., 2019. A review of fuzzy logic applications in petroleum exploration, production and distribution operations. Journal of Petroleum Exploration and Production Technology, 9: 1555–1568.
• Okwu M.O., Olufemi A., 2018. A comparative study of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) model in distribution system with nondeterministic inputs. International Journal of Engineering and Business Management SAGE, 10(12): 1–17. DOI:10.1177/1847979018768421.
• Raymer L.L., Hunt E.R., Gardner J.S., 1980. An improved sonic transit Time-to-porosity transform. Proceedings of Society of Petrophysicists and Well-Log Analysts, Houston, 1–13.
• Souza J., Rostirolla S.R., 2011. A fast MATLAB program to estimate the multifractal spectrum of multidimensional data: Application to fractures. Computers and Geosciences, 37(2): 241–249. DOI:10.1016/j.cageo.2010.09.001.
• Ullah K., Arif M., Shah M.T., 2006. Petrography of sandstones from the Kamlial and Chinji formations, southwestern Kohat plateau, NW Pakistan: implications for source lithology and paleoclimate. Journal of Himalayan Earth Sciences, 39: 1–13.
• Ullah S., Jan I.U., Hanif M., Latif K., Mohibullah M., Sabba M., Sabba M., Anees A., Ashraf U., Thanh H.V., 2022. Paleoenvironmental and bio-sequence stratigraphic analysis of the cretaceous pelagic carbonates of eastern Tethys, Sulaiman range, Pakistan. Minerals, 12(8): 946. DOI: 10.3390/min12080946.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).