Research Article
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Year 2024, Volume: 6 Issue: 1, 1 - 11, 15.05.2024

Abstract

References

  • Anemangely, M., Ramezanzadeh, A., Amiri, H., Hoseinpour, S-A., 2019. Machine learning technique for the prediction of shear wave velocity using petrophysical logs. Journal of Petroleum Science and Engineering 174, 306-327. https://doi.org/10.1016/j.petrol.2018.11.032.
  • Aihar, A., Bouabdallah, N., Ifrene, G., Irofti, D., 2023. Comparing Fishbone Drilling and Hydraulic Fracturing in Ultra-Low Permeability Geothermal Reservoirs.
  • Allaoui, A., Belksier, M. S., Ameur-Zaimeche, O., Kechiched, R., Remita, A., Fellah, L., Lamouri, B., Habes, S., 2022. The lower Silurian black Shales from the Ahnet basin (SW Algerian Saharan platform): a comprehensive mineralogical study and paleoenvironmental implications. Arabian Journal of Geosciences 15 (11), 1103. https://doi.org/10.1007/s12517-022-10388-9.
  • Beuf, S., 1971. Gres Du Paleozoique Inferieur Au SaharA (Issue 18). editions Technip.
  • Bressan, T.S., de Souza, M.K., Girelli, T.J., Junior, F.C., 2020. Evaluation of machine learning methods for lithology classification using geophysical data. In Computers and Geosciences 139, 104475. https://doi.org/10.1016/j.cageo.2020.104475.
  • Brocher, T.M., 2005. Empirical Relations Between Elastic Wavespeeds and Density in the Earth’s Crust. Bulletin of the Seismological Society of America 95 (6), 2081-2092. https://doi.org/10.1785/0120050077.
  • Castagna, J.P., Batzle, M.L., Eastwood, R.L., 1985. Relationships between compressional‐wave and shear‐wave velocities in clastic silicate rocks. Geophysıcs 50 (4), 571-581. https://doi.org/10.1190/1.1441933.
  • Chaikine, I.A., Ian, S., Gates, D., 2020. A New Machine Learning Procedure to Generate Highly Accurate Synthetic Shear Sonic Logs in Unconventional Reservoirs. http://onepetro.org/SPEATCE/proceedings-pdf/20ATCE/3-20ATCE/D031S027R007/2372201/spe-201453-ms.pdf/1.
  • Chellal, H.A.K., Egenhoff, S., Latrach, A., Bakelli, O., 2023. Machine Learning Based Predictive Models for UCS and Young’s Modulus of the Dakota Sand Using Schmidt Hammer Rebound. In All Days. 57th U.S. Rock Mechanics/Geomechanics Symposium. ARMA. https://doi.org/10.56952/arma-2023-0819.
  • Chemmakh, A., Merzoug, A., Ouadi, H., Ladmia, A., Rasouli, V., 2021. Machine Learning Predictive Models to Estimate the Minimum Miscibility Pressure of CO2-Oil System. In Day 3 Wed, November 17, 2021. Abu Dhabi International Petroleum Exhibition & Conference. SPE. https://doi.org/10.2118/207865-ms.
  • Fabricio, O.A.A., Beneduzi, C.F., Rossi, T.B., Seabra, C.E., 2015. Shear Wave Velocity Estimation in slow siliciclastic formations using empirical models. 14th International Congress of the Brazilian Geophysical Society, held in Rio de Janeiro, Brazil, August 3-6 2015.
  • Greenberg, M.L., Castagna, J.P., 1992. Shear‐Wave Velocıty Estımatıon In Porous Rocks: Theoretıcal Formulatıon, Prelımınary Verıfıcatıon And Applıcatıons. Geophysical Prospecting 40 (2), 195-209. https://doi.org/10.1111/j.1365-2478.1992.tb00371.x.
  • Han, D., 1987. Effects of porosity and clay content on acoustic properties of sandstones and unconsolidated sediments. Stanford University.
  • Hamadi, M., El Mehadji, T., Laalam, A., Zeraibi, N., Tomomewo, O.S., Ouadi, H., Dehdouh, A., 2023. Prediction of Key Parameters in the Design of CO2 Miscible Injection via the Application of Machine Learning Algorithms. Eng 2023, 4, 1905-1932. https://doi.org/10.3390/eng4030108.
  • Irofti, D., Ifrene, G., Cheddad, F.A., Djemai, S., 2023. Integrating Borehole Imaging and Full Waveform Dipole Sonic Data to Estimate Fracture Porosity in Tight Formations: A Workflow for Accurate Characterization of Natural Fractures.
  • Irofti, D., Ifrene, G.E., Aihar, A., Bouabdallah, N., Khouissat, A., Djemai, S., 2023. Characterization of a Tight Gas Reservoir Using the Integration of Electrofacies and Fracture Aperture, Ahnet, Algeria. In 57th US Rock Mechanics/Geomechanics Symposium. OnePetro.
  • Irofti, D., Ifrene, G.E., Pu, H., Djemai, S., 2022. A Multiscale Approach to Investigate Hydraulic Attributes of Natural Fracture Networks in Two Tight Sandstone Fields, Ahnet, Algeria. In ARMA US Rock Mechanics/Geomechanics Symposium (pp. ARMA-2022).
  • Ifrene, G.E., Irofti, D., Khetib, Y., Rasouli, V., 2022. Shear Waves Anisotropy and Image Logs Integration for Improved Fracture Characterization. In ARMA US Rock Mechanics/Geomechanics Symposium (pp. ARMA-2022). ARMA.
  • Ifrene, G., Irofti, D., Ni, R., Egenhoff, S., Pothana, P., 2023. New Insights into Fracture Porosity Estimations Using Machine Learning and Advanced Logging Tools. Fuels, 4 (3), 333-353.
  • Jiang, R., Ji, Z., Mo, W., Wang, S., Zhang, M., Yin, W., Wang, Z., Lin, Y., Wang, X., Ashraf, U., 2022. A Novel Method of Deep Learning for Shear Velocity Prediction in a Tight Sandstone Reservoir. Energies, 15 (19). https://doi.org/10.3390/en15197016.
  • Josephs, R.E., Porlles, J., Tomomewo, O.S., Gyimah, E., Ebere, F., 2023. Geo-Mechanical Characterization of a Well to Store Hydrogen. Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, Atlanta, Georgia, USA, June 2023. https://doi.org/10.56952/ARMA-2023-0528.
  • Kadri, M.M., Hacini, M., 2018. Preliminary Reservoirs Characterizations of Silurian Shale, Case of Ahnet Basin, Southern Algeria. International Journal of Latest Research in Engineering and Management 2 (2), 29-33.
  • Karrenbach, M., Essenreiter, R., Treitel, S., 2000. Multiple attenuation with attribute‐based neural networks. In SEG Technical Program Expanded Abstracts 2000. SEG Technical Program Expanded Abstracts 2000. Society of Exploration Geophysicists. https://doi.org/10.1190/1.1815827.
  • Lee, M.W., 2013. Comparison of Methods for Predicting Shear-Wave Velocities of Unconsolidated Shallow Sediments in the Gulf of Mexico. http://pubs.usgs.gov/sir/2013/5141/.
  • Lüning, S., Wendt, J., Belka, Z., Kaufmann, B., 2004. Temporal–spatial reconstruction of the early Frasnian (Late Devonian) anoxia in NW Africa: new field data from the Ahnet Basin (Algeria). Sedimentary Geology 163 (3-4), 237–264). https://doi.org/10.1016/s0037-0738(03)00210-0.
  • Latrach, A., Malki, M.L., Morales, M., Mehana, M., Rabiei, M., 2023c. A Critical Review of Physics-Informed Machine Learning Applications in Subsurface Energy Systems (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2308.04457.
  • Latrach, A., 2023b. Application of Deep Learning for Predictive Maintenance of Oilfield Equipment. arXiv. https://doi.org/10.48550/ARXIV.2306.11040.
  • Latrach, A., Merzoug, A., Abdelhamid, C., Mellal, I., Rabiei, M., 2023a. Identification and Quantification of the Effect of Fracture-Driven Interactions on Production From Parent and Child Wells in Williston Basin. In Proceedings of the 11th Unconventional Resources Technology Conference. Unconventional Resources Technology Conference. American Association of Petroleum Geologists. https://doi.org/10.15530/urtec-2023-3868861.
  • Laoufi, H., Megherbi, Z., Zeraibi, N., Merzoug, A., Ladmia, A. 2022. Selection of Sand Control Completion Techniques Using Machine Learning. In All Days. International Geomechanics Symposium. ARMA. https://doi.org/10.56952/igs-2022-118.
  • LeCun, Y., Bengio, Y., Hinton, G.. 2015. Deep learning. In Nature (Vol. 521, Issue 7553, pp. 436–444). Springer Science and Business Media LLC. https://doi.org/10.1038/nature14539.
  • Liu, B., 2017. Lifelong machine learning: a paradigm for continuous learning. In Frontiers of Computer Science (Vol. 11, Issue 3, pp. 359-361). Springer Science and Business Media LLC. https://doi.org/10.1007/s11704-016-6903-6.
  • Mellal, I., Latrach, A., Rasouli, V., Bakelli, O., Dehdouh, A., Ouadi, H., 2023. Water Saturation Prediction in the Middle Bakken Formation Using Machine Learning. Eng 4 (3), 1951-1964. https://doi.org/10.3390/eng4030110.
  • Mellal, I., Malki, M., Latrach, A., Ameur-Zaimech, O., Bakelli, O., 2023. Multiscale Formation Evaluation and Rock Types Identification in The Middle Bakken Formation. SPWLA 64 Th Annual Logging Symposium. https://doi.org/10.30632/SPWLA-2023-0012.
  • Mellal, I., Rasouli, V., Dehdouh, A., Letrache, A., Abdelhamid, C., Malki, M.L., Bakelli, O., 2023. Formation Evaluation Challenges of Tight and Shale Reservoirs. A Case Study of the Bakken Petroleum System." Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, Atlanta, Georgia, USA, June 2023. https://doi.org/10.56952/ARMA-2023-0894.
  • Miah, M.I., 2021. Improved prediction of shear wave velocity for clastic sedimentary rocks using hybrid model with core data. Journal of Rock Mechanics and Geotechnical Engineering, 13 (6), 1466-1477. https://doi.org/10.1016/j.jrmge.2021.06.014.
  • Mofredj, I., Nedjari, A., 2019. Le Dévonien Inférieur De L'ahnet Occidental- Bled El Mass (Sahara Algérien), Formations Et Environnements. Mémoire du Service Géologique de l’Algérie 20, 71-91.
  • Moran, D., Ibrahim, H., Purwanto, A., Osmond, J., 2010. Sophisticated ROP Prediction Technologies Based on Neural Network Delivers Accurate Drill Time Results. In All Days. IADC/SPE Asia Pacific Drilling Technology Conference and Exhibition. SPE. https://doi.org/10.2118/132010-ms.
  • Ojha, M., Sain, K., 2014. Velocity-Porosity and Velocity-Density Relationship for Shallow Sediments in the Kerala-Konkan Basin of Western Indian Margin. Journal of the Geological Society of India 84 (2), 187-191. https://doi.org/10.1007/s12594-014-0122-2.
  • Ouadi, H., Mishani, S., Rasouli, V., 2023. Applications of Underbalanced Fishbone Drilling for Improved Recovery and Reduced Carbon Footprint in Unconventional Plays. Petroleum & Petrochemical Engineering Journal 7 (1), 1-23. https://doi.org/10.23880/ppej-16000331.
  • Ouadi, H., Mellal, I., Chemmakh, A., Djezzar, S., Boualam, A., Merzoug, A., Laalem, A., Mouedden, N., Khetib, Y., and Rasouli, V., 2022. New Approach for Stress-Dependent Permeability and Porosity Response in the Bakken Formation. SPE Annual Technical Conference and Exhibition 2022-October. https://doi.org/10.2118/210104-MS.
  • Shawaf, A., Rasouli, V., Dehdouh, A., 2023. The Impact of Formation Anisotropy and Stresses on Fractural Geometry—A Case Study in Jafurah’s Tuwaiq Mountain Formation (TMF), Saudi Arabia. Processes 2023, 11, 1545. https://doi.org/10.3390/pr11051545.
  • Shawaf, A., Rasouli, V., Dehdouh, A., 2023. Applications of Differential Effective Medium (DEM)-Driven Correlations to Estimate Elastic Properties of Jafurah Tuwaiq Mountain Formation (TMF). Processes 2023, 11, 1643. https://doi.org/10.3390/pr11061643.
  • Sohail, G.M., Hawkes, C.D., 2020. An evaluation of empirical and rock physics models to estimate shear wave velocity in a potential shale gas reservoir using wireline logs. Journal of Petroleum Science and Engineering, 185. https://doi.org/10.1016/j.petrol.2019.106666.
  • Suleymanov, V., Gamal, H., Elkatatny, S., Glatz, G., Abdulraheem, A., 2022. Machine Learning Models for Acoustic Data Prediction During Drilling Composite Lithology Formations. Journal of Energy Resources Technology, Transactions of the ASME, 144 (10). https://doi.org/10.1115/1.4053846.
  • Suleymanov, V., Gamal, H., Glatz, G., Elkatatny, S., Abdulraheem, A., Fahd, K., 2021. Real-Time Prediction for Sonic Slowness Logs from Surface Drilling Data Using Machine Learning Techniques. Paper presented at the SPE Annual Caspian Technical Conference, Virtual, October 2021. Paper Number: SPE-207000-MS. https://doi.org/10.2118/207000-MS.
  • Zhang, Y., Zhong, H.R., Wu, Z.Y., Zhou, H., Ma, Q.Y., 2020. Improvement of petrophysical workflow for shear wave velocity prediction based on machine learning methods for complex carbonate reservoirs. Journal of Petroleum Science and Engineering 192, 107234. https://doi.org/10.1016/j.petrol.2020.107234.

Enhancing Shear Velocity Log Prediction in Ahnet Field, Algeria, Through Well Logs and Machine Learning Techniques

Year 2024, Volume: 6 Issue: 1, 1 - 11, 15.05.2024

Abstract

Shear velocity logs are crucial in the oil and gas industry for assessing subsurface mechanical properties, including rock stiffness, shear strength, and seismic wave propagation, essential for optimizing hydrocarbon exploration and production strategies. However, obtaining shear velocity logs conventionally is expensive and time-consuming, especially when drilling additional wells solely for this purpose. With the recent boom in machine learning algorithms adoption across various scientific domains, it proved to be an extremely valuable tool for numerous applications in the oil and gas industry. It makes use of the readily available large datasets collected over decades and leverages this data to train powerful, data-driven models, reducing the reliance on empirical relationships that usually have poor generalization. This study follows this approach and presents the use and comparison of machine learning algorithms for predicting shear velocity logs from conventional and readily available logs in the Ahnet field, Algeria. Ultimately, this study aims to enhance reservoir assessment and optimize hydrocarbon recovery processes, potentially reducing exploration costs and improving oil and gas production decision-making in the region.

References

  • Anemangely, M., Ramezanzadeh, A., Amiri, H., Hoseinpour, S-A., 2019. Machine learning technique for the prediction of shear wave velocity using petrophysical logs. Journal of Petroleum Science and Engineering 174, 306-327. https://doi.org/10.1016/j.petrol.2018.11.032.
  • Aihar, A., Bouabdallah, N., Ifrene, G., Irofti, D., 2023. Comparing Fishbone Drilling and Hydraulic Fracturing in Ultra-Low Permeability Geothermal Reservoirs.
  • Allaoui, A., Belksier, M. S., Ameur-Zaimeche, O., Kechiched, R., Remita, A., Fellah, L., Lamouri, B., Habes, S., 2022. The lower Silurian black Shales from the Ahnet basin (SW Algerian Saharan platform): a comprehensive mineralogical study and paleoenvironmental implications. Arabian Journal of Geosciences 15 (11), 1103. https://doi.org/10.1007/s12517-022-10388-9.
  • Beuf, S., 1971. Gres Du Paleozoique Inferieur Au SaharA (Issue 18). editions Technip.
  • Bressan, T.S., de Souza, M.K., Girelli, T.J., Junior, F.C., 2020. Evaluation of machine learning methods for lithology classification using geophysical data. In Computers and Geosciences 139, 104475. https://doi.org/10.1016/j.cageo.2020.104475.
  • Brocher, T.M., 2005. Empirical Relations Between Elastic Wavespeeds and Density in the Earth’s Crust. Bulletin of the Seismological Society of America 95 (6), 2081-2092. https://doi.org/10.1785/0120050077.
  • Castagna, J.P., Batzle, M.L., Eastwood, R.L., 1985. Relationships between compressional‐wave and shear‐wave velocities in clastic silicate rocks. Geophysıcs 50 (4), 571-581. https://doi.org/10.1190/1.1441933.
  • Chaikine, I.A., Ian, S., Gates, D., 2020. A New Machine Learning Procedure to Generate Highly Accurate Synthetic Shear Sonic Logs in Unconventional Reservoirs. http://onepetro.org/SPEATCE/proceedings-pdf/20ATCE/3-20ATCE/D031S027R007/2372201/spe-201453-ms.pdf/1.
  • Chellal, H.A.K., Egenhoff, S., Latrach, A., Bakelli, O., 2023. Machine Learning Based Predictive Models for UCS and Young’s Modulus of the Dakota Sand Using Schmidt Hammer Rebound. In All Days. 57th U.S. Rock Mechanics/Geomechanics Symposium. ARMA. https://doi.org/10.56952/arma-2023-0819.
  • Chemmakh, A., Merzoug, A., Ouadi, H., Ladmia, A., Rasouli, V., 2021. Machine Learning Predictive Models to Estimate the Minimum Miscibility Pressure of CO2-Oil System. In Day 3 Wed, November 17, 2021. Abu Dhabi International Petroleum Exhibition & Conference. SPE. https://doi.org/10.2118/207865-ms.
  • Fabricio, O.A.A., Beneduzi, C.F., Rossi, T.B., Seabra, C.E., 2015. Shear Wave Velocity Estimation in slow siliciclastic formations using empirical models. 14th International Congress of the Brazilian Geophysical Society, held in Rio de Janeiro, Brazil, August 3-6 2015.
  • Greenberg, M.L., Castagna, J.P., 1992. Shear‐Wave Velocıty Estımatıon In Porous Rocks: Theoretıcal Formulatıon, Prelımınary Verıfıcatıon And Applıcatıons. Geophysical Prospecting 40 (2), 195-209. https://doi.org/10.1111/j.1365-2478.1992.tb00371.x.
  • Han, D., 1987. Effects of porosity and clay content on acoustic properties of sandstones and unconsolidated sediments. Stanford University.
  • Hamadi, M., El Mehadji, T., Laalam, A., Zeraibi, N., Tomomewo, O.S., Ouadi, H., Dehdouh, A., 2023. Prediction of Key Parameters in the Design of CO2 Miscible Injection via the Application of Machine Learning Algorithms. Eng 2023, 4, 1905-1932. https://doi.org/10.3390/eng4030108.
  • Irofti, D., Ifrene, G., Cheddad, F.A., Djemai, S., 2023. Integrating Borehole Imaging and Full Waveform Dipole Sonic Data to Estimate Fracture Porosity in Tight Formations: A Workflow for Accurate Characterization of Natural Fractures.
  • Irofti, D., Ifrene, G.E., Aihar, A., Bouabdallah, N., Khouissat, A., Djemai, S., 2023. Characterization of a Tight Gas Reservoir Using the Integration of Electrofacies and Fracture Aperture, Ahnet, Algeria. In 57th US Rock Mechanics/Geomechanics Symposium. OnePetro.
  • Irofti, D., Ifrene, G.E., Pu, H., Djemai, S., 2022. A Multiscale Approach to Investigate Hydraulic Attributes of Natural Fracture Networks in Two Tight Sandstone Fields, Ahnet, Algeria. In ARMA US Rock Mechanics/Geomechanics Symposium (pp. ARMA-2022).
  • Ifrene, G.E., Irofti, D., Khetib, Y., Rasouli, V., 2022. Shear Waves Anisotropy and Image Logs Integration for Improved Fracture Characterization. In ARMA US Rock Mechanics/Geomechanics Symposium (pp. ARMA-2022). ARMA.
  • Ifrene, G., Irofti, D., Ni, R., Egenhoff, S., Pothana, P., 2023. New Insights into Fracture Porosity Estimations Using Machine Learning and Advanced Logging Tools. Fuels, 4 (3), 333-353.
  • Jiang, R., Ji, Z., Mo, W., Wang, S., Zhang, M., Yin, W., Wang, Z., Lin, Y., Wang, X., Ashraf, U., 2022. A Novel Method of Deep Learning for Shear Velocity Prediction in a Tight Sandstone Reservoir. Energies, 15 (19). https://doi.org/10.3390/en15197016.
  • Josephs, R.E., Porlles, J., Tomomewo, O.S., Gyimah, E., Ebere, F., 2023. Geo-Mechanical Characterization of a Well to Store Hydrogen. Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, Atlanta, Georgia, USA, June 2023. https://doi.org/10.56952/ARMA-2023-0528.
  • Kadri, M.M., Hacini, M., 2018. Preliminary Reservoirs Characterizations of Silurian Shale, Case of Ahnet Basin, Southern Algeria. International Journal of Latest Research in Engineering and Management 2 (2), 29-33.
  • Karrenbach, M., Essenreiter, R., Treitel, S., 2000. Multiple attenuation with attribute‐based neural networks. In SEG Technical Program Expanded Abstracts 2000. SEG Technical Program Expanded Abstracts 2000. Society of Exploration Geophysicists. https://doi.org/10.1190/1.1815827.
  • Lee, M.W., 2013. Comparison of Methods for Predicting Shear-Wave Velocities of Unconsolidated Shallow Sediments in the Gulf of Mexico. http://pubs.usgs.gov/sir/2013/5141/.
  • Lüning, S., Wendt, J., Belka, Z., Kaufmann, B., 2004. Temporal–spatial reconstruction of the early Frasnian (Late Devonian) anoxia in NW Africa: new field data from the Ahnet Basin (Algeria). Sedimentary Geology 163 (3-4), 237–264). https://doi.org/10.1016/s0037-0738(03)00210-0.
  • Latrach, A., Malki, M.L., Morales, M., Mehana, M., Rabiei, M., 2023c. A Critical Review of Physics-Informed Machine Learning Applications in Subsurface Energy Systems (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2308.04457.
  • Latrach, A., 2023b. Application of Deep Learning for Predictive Maintenance of Oilfield Equipment. arXiv. https://doi.org/10.48550/ARXIV.2306.11040.
  • Latrach, A., Merzoug, A., Abdelhamid, C., Mellal, I., Rabiei, M., 2023a. Identification and Quantification of the Effect of Fracture-Driven Interactions on Production From Parent and Child Wells in Williston Basin. In Proceedings of the 11th Unconventional Resources Technology Conference. Unconventional Resources Technology Conference. American Association of Petroleum Geologists. https://doi.org/10.15530/urtec-2023-3868861.
  • Laoufi, H., Megherbi, Z., Zeraibi, N., Merzoug, A., Ladmia, A. 2022. Selection of Sand Control Completion Techniques Using Machine Learning. In All Days. International Geomechanics Symposium. ARMA. https://doi.org/10.56952/igs-2022-118.
  • LeCun, Y., Bengio, Y., Hinton, G.. 2015. Deep learning. In Nature (Vol. 521, Issue 7553, pp. 436–444). Springer Science and Business Media LLC. https://doi.org/10.1038/nature14539.
  • Liu, B., 2017. Lifelong machine learning: a paradigm for continuous learning. In Frontiers of Computer Science (Vol. 11, Issue 3, pp. 359-361). Springer Science and Business Media LLC. https://doi.org/10.1007/s11704-016-6903-6.
  • Mellal, I., Latrach, A., Rasouli, V., Bakelli, O., Dehdouh, A., Ouadi, H., 2023. Water Saturation Prediction in the Middle Bakken Formation Using Machine Learning. Eng 4 (3), 1951-1964. https://doi.org/10.3390/eng4030110.
  • Mellal, I., Malki, M., Latrach, A., Ameur-Zaimech, O., Bakelli, O., 2023. Multiscale Formation Evaluation and Rock Types Identification in The Middle Bakken Formation. SPWLA 64 Th Annual Logging Symposium. https://doi.org/10.30632/SPWLA-2023-0012.
  • Mellal, I., Rasouli, V., Dehdouh, A., Letrache, A., Abdelhamid, C., Malki, M.L., Bakelli, O., 2023. Formation Evaluation Challenges of Tight and Shale Reservoirs. A Case Study of the Bakken Petroleum System." Paper presented at the 57th U.S. Rock Mechanics/Geomechanics Symposium, Atlanta, Georgia, USA, June 2023. https://doi.org/10.56952/ARMA-2023-0894.
  • Miah, M.I., 2021. Improved prediction of shear wave velocity for clastic sedimentary rocks using hybrid model with core data. Journal of Rock Mechanics and Geotechnical Engineering, 13 (6), 1466-1477. https://doi.org/10.1016/j.jrmge.2021.06.014.
  • Mofredj, I., Nedjari, A., 2019. Le Dévonien Inférieur De L'ahnet Occidental- Bled El Mass (Sahara Algérien), Formations Et Environnements. Mémoire du Service Géologique de l’Algérie 20, 71-91.
  • Moran, D., Ibrahim, H., Purwanto, A., Osmond, J., 2010. Sophisticated ROP Prediction Technologies Based on Neural Network Delivers Accurate Drill Time Results. In All Days. IADC/SPE Asia Pacific Drilling Technology Conference and Exhibition. SPE. https://doi.org/10.2118/132010-ms.
  • Ojha, M., Sain, K., 2014. Velocity-Porosity and Velocity-Density Relationship for Shallow Sediments in the Kerala-Konkan Basin of Western Indian Margin. Journal of the Geological Society of India 84 (2), 187-191. https://doi.org/10.1007/s12594-014-0122-2.
  • Ouadi, H., Mishani, S., Rasouli, V., 2023. Applications of Underbalanced Fishbone Drilling for Improved Recovery and Reduced Carbon Footprint in Unconventional Plays. Petroleum & Petrochemical Engineering Journal 7 (1), 1-23. https://doi.org/10.23880/ppej-16000331.
  • Ouadi, H., Mellal, I., Chemmakh, A., Djezzar, S., Boualam, A., Merzoug, A., Laalem, A., Mouedden, N., Khetib, Y., and Rasouli, V., 2022. New Approach for Stress-Dependent Permeability and Porosity Response in the Bakken Formation. SPE Annual Technical Conference and Exhibition 2022-October. https://doi.org/10.2118/210104-MS.
  • Shawaf, A., Rasouli, V., Dehdouh, A., 2023. The Impact of Formation Anisotropy and Stresses on Fractural Geometry—A Case Study in Jafurah’s Tuwaiq Mountain Formation (TMF), Saudi Arabia. Processes 2023, 11, 1545. https://doi.org/10.3390/pr11051545.
  • Shawaf, A., Rasouli, V., Dehdouh, A., 2023. Applications of Differential Effective Medium (DEM)-Driven Correlations to Estimate Elastic Properties of Jafurah Tuwaiq Mountain Formation (TMF). Processes 2023, 11, 1643. https://doi.org/10.3390/pr11061643.
  • Sohail, G.M., Hawkes, C.D., 2020. An evaluation of empirical and rock physics models to estimate shear wave velocity in a potential shale gas reservoir using wireline logs. Journal of Petroleum Science and Engineering, 185. https://doi.org/10.1016/j.petrol.2019.106666.
  • Suleymanov, V., Gamal, H., Elkatatny, S., Glatz, G., Abdulraheem, A., 2022. Machine Learning Models for Acoustic Data Prediction During Drilling Composite Lithology Formations. Journal of Energy Resources Technology, Transactions of the ASME, 144 (10). https://doi.org/10.1115/1.4053846.
  • Suleymanov, V., Gamal, H., Glatz, G., Elkatatny, S., Abdulraheem, A., Fahd, K., 2021. Real-Time Prediction for Sonic Slowness Logs from Surface Drilling Data Using Machine Learning Techniques. Paper presented at the SPE Annual Caspian Technical Conference, Virtual, October 2021. Paper Number: SPE-207000-MS. https://doi.org/10.2118/207000-MS.
  • Zhang, Y., Zhong, H.R., Wu, Z.Y., Zhou, H., Ma, Q.Y., 2020. Improvement of petrophysical workflow for shear wave velocity prediction based on machine learning methods for complex carbonate reservoirs. Journal of Petroleum Science and Engineering 192, 107234. https://doi.org/10.1016/j.petrol.2020.107234.
There are 46 citations in total.

Details

Primary Language English
Subjects Marine Geology and Geophysics
Journal Section Research Article
Authors

Riadh Goucem This is me

Publication Date May 15, 2024
Submission Date April 4, 2024
Acceptance Date May 1, 2024
Published in Issue Year 2024 Volume: 6 Issue: 1

Cite

AMA Goucem R. Enhancing Shear Velocity Log Prediction in Ahnet Field, Algeria, Through Well Logs and Machine Learning Techniques. IJESKA. May 2024;6(1):1-11.