Assessing the Viability of Gandhar Field in India’s Cambay Basin for CO₂ Storage

Abstract

Our research is centered on the Gandhar oil field, which was discovered in 1983, where daily oil production has declined significantly over the years. The primary objective was to evaluate the feasibility of carbon dioxide (CO₂) storage through its injection into the siliciclastic reservoirs of Ankleshwar Formation. We aimed to obtain high-resolution acoustic impedance data to estimate porosity employing model-based poststack seismic inversion. We conducted an analysis of the density and effective porosity in the target zone through geostatistical techniques and probabilistic neural networks. Simultaneously, the work also involved geomechanical analysis through the computation of pore pressure and fracture gradient using well-log data, geological information, and drilling events in the Gandhar field. Our investigation unveiled spatial variations in effective porosity within the Hazad Member of the Ankleshwar Formation, with an effective porosity exceeding 25% observed in several areas, which indicates the presence of well-connected pore spaces conducive to efficient CO₂ migration. Geomechanical analysis showed that the vertical stress (Sv) ranged from 55 MPa to 57 MPa in Telwa and from 63.7 MPa to 67.7 MPa in Hazad Member. The pore pressure profile displayed variations along the stratigraphic sequence, with the shale zone, particularly in the Kanwa Formation, attaining the maximum pressure gradient (approximately 36 MPa). However, consistently low pore pressure values (30–34 MPa) considerably below the fracture gradient curves were observed in Hazad Member due to depletion. The results from our analysis provide valuable insights into shaping future field development strategies and exploration of the feasibility of CO₂ sequestration in Gandhar Field.

Keywords

Carbon capture and storage / Reservoir characterization / Seismic inversion / Geomechanics / CO₂ storage / CO₂ enhancing oil recovery

References

[1] Abdulaziz AM, Mahdi HA, Sayyouh MH. Prediction of reservoir quality using well logs and seismic attributes analysis with an artificial neural network: A case study from Farrud Reservoir, Al-Ghani Field, Libya. J Appl Geophy, 2019, 161: 239-254

[2] Agarwal B, Singh H, Tandon AN, Upadhyaya PK, Ram J. Detection of thin sand by using seismic inversion in Gandhar field of Cambay Basin, India-A case study. 10th Biennial International Conference & Exposition, 2013

[3] Aswal HS, Das KK, Yadava UN, Nayak KK, Prathimon PT, Rana P. Lithobiostratigraphic Correlation and Paleoenvironment of Hazad Pays in Eastern Part of Jambusar–Broach Block, Cambay Basin. SPG’s 10^th Biennial International Conference & Exposition, 2013

[4] Austin O, Samuel IO, Agbasi O, Abdulrazzaq ZT. Application of model-based inversion technique in a field in the coastal swamp depobelt, Niger delta. International Journal of Advanced Geosciences, 2018, 6: 122

[5] Bachu S. Review of CO₂ storage efficiency in deep saline aquifers. International Journal of Greenhouse Gas Control, 2015, 40: 188-202

[6] Bachu S. Identification of oil reservoirs suitable for CO₂-EOR and CO₂ storage (CCUS) using reserves databases, with application to Alberta, Canada. International Journal of Greenhouse Gas Control, 2016, 44: 152-165

[7] Biswas SK, Rangaraju MK, Thomas J, Bhattacharya SK. Cambay–Hazad(!) Petroleum System in the South Cambay Basin, India. The Petroleum System — From Source to Trap, 1994, 615-624

[8] Callas C, Saltzer SD, Steve Davis J, Hashemi SS, Kovscek AR, Okoroafor ER, Wen G, Zoback MD, Benson SM. Criteria and workflow for selecting depleted hydrocarbon reservoirs for carbon storage. Appl Energy, 2022, 324: 119668

[9] Chandra D, Vishal V. A Comparative Analysis of Pore Attributes of Sub-Bituminous Gondwana Coal from the Damodar and Wardha Valleys: Implication for Enhanced Coalbed Methane Recovery. Energy & Fuels, 2022, 36: 6187-6197

[10] Eaton BA. The Equation for Geopressure Prediction from Well Logs. Fall Meeting of the Society of Petroleum Engineers of AIME, 1975, Dallas, Texas: SPE

[11] Freedman R, Boyd A, Gubelin G, Mckeon D, Morriss CE, Wireline S, Flaum TC. Measurement Of Total Nmr Porosity Adds New Value To Nmr Logging. SPWLA 38th Annual Logging Symposium, 1997

[12] Hampson D, Todorov T, Russell B. Using multi-attribute transforms to predict log properties from seismic data, 2018, 31: 481-487

[13] Jafari M, Nikrouz R, Kadkhodaie A. Estimation of acoustic-impedance model by using model-based seismic inversion on the Ghar Member of Asmari Formation in an oil field in southwestern Iran. The Leading Edge, 2017, 36: 487-492

[14] Jaiswal S, Bhattacharya B, Chakrabarty S. High resolution sequence stratigraphy of middle Eocene Hazad member, Jambusar-broach block, Cambay Basin, India. Marine and Petroleum Geology, 2018, 93: 79-94

[15] Kumar A, Narayan JP, Lakhera S, Chattopadhyay T, Verma RP, Unit G-S. Analysis of GS-11 Low-Resistivity Pay in Main Gandhar Field, Cambay Basin, India-A Case Study. SPG’s 5th Conference & Exposition on Petroleum Geophysics, 2004, 162-166

[16] Latimer RB (2011) 9. Inversion and Interpretation of Impedance Data. Investigations in Geophysics Series 309–350. https://doi.org/10.1190/1.9781560802884.CH9

[17] Malik S, Makauskas P, Karaliūtė V, Pal M, Sharma R. Assessing the geological storage potential of CO₂ in Baltic Basin: A case study of Lithuanian hydrocarbon and deep saline reservoirs. Int. J. Greenh. Gas Control, 2024, 133: 104097

[18] Malik S, Makauskas P, Sharma R, Pal M. Exploring CO₂ storage potential in Lithuanian deep saline aquifers using digital rock volumes: a machine learning guided approach. Baltic Carbon Forum, 2023, 13-14

[19] Maurya SP, Singh KH. Reservoir Characterization using Model Based Inversion and Probabilistic Neural Network. Discovery, 2015, 49(228): 122-127

[20] Maurya SP, Singh KH (2019) Predicting Porosity by Multivariate Regression and Probabilistic Neural Network using Model-based and Coloured Inversion as External Attributes: A Quantitative Comparison. Journal Geological Society of India: 207–212. https://doi.org/10.1007/s12594-019-1153-5

[21] Maurya SP, Singh NP, Singh KH. Seismic Inversion Methods: A Practical Approach, 2020

[22] Mishra GK, Meena RK, Mitra S, Saha K, Dhakate VP, Prakash O, Singh RK. Planning India’s First CO2-EOR Project as Carbon Capture Utilization & Storage: A Step Towards Sustainable Growth. SPE OGIC 2019, 2019

[23] MoPNG Draft 2030 Roadmap for Carbon Capture Utilization and Storage for Upstream E&P companies, 2022, New Delhi, India: Ministry of Petroleum and Natural Gas, Govt. of India

[24] Mukherjee A, Chatterjee S. Carbon Capture, Utilization and Storage (CCUS) -Policy Framework and its Deployment Mechanism in India, 2022, New Delhi, India: NITI Aayog

[25] Pal M, Karaliūtė V, Malik S. Exploring the Potential of Carbon Capture, Utilization, and Storage in Baltic Sea Region Countries: A Review of CCUS Patents from 2000 to 2022. Processes, 2023, 11: 605

[26] Raza A, Rezaee R, Gholami R, Bing CH, Nagarajan R, Hamid MA. A screening criterion for selection of suitable CO₂ storage sites. J Nat Gas Sci Eng, 2016, 28: 317-327

[27] Russell B. Introduction to Seismic Inversion Methods, 1988, Tulsa, USA: Society of Exploration Geophysicists

[28] Russell B, Hampson D. The old and the new in seismic inversion. CSEG Recorder, 2006, 31(10): 5-11

[29] Saha P, Arasu R, Rahaman M, Tiwari D, Josyulu B. Post-rift Structural Evolution Of Gandhar-Nada Area And Its Implication On Hydrocarbon Entrapment In Broach-Jambusar Block, Cambay Basin, India. SPG’s 5^th Conference & Exposition on Petroleum Geophysics, 2004, 413-422

[30] Shao-wu G, Bo Z, Zhen-hua H, Yu-ning M, Shao-wu G, Bo Z, Zhen-hua H, Yu-ning M. Research progress of seismic wavelet extraction. Progress in Geophysics, 2009, 24(4): 1384-1391

[31] Sheriff RE. Encyclopedic dictionary of applied geophysics, 2002, Tulsa, USA: Society of Exploration Geophysicists

[32] Szulczewski ML, MacMinn CW, Juanes R. Theoretical analysis of how pressure buildup and CO₂ migration can both constrain storage capacity in deep saline aquifers. International Journal of Greenhouse Gas Control, 2014, 23: 113-118

[33] Tariq Z, Gudala M, Xu Z, . An Effective Method of Estimating Nuclear Magnetic Resonance Based Porosity Using Deep Learning Approach, 2022, Abu Dhabi, UAE: ADIPEC. SPE, SPE-211360-MS

[34] Terzaghi K. Theoretical Soil Mechanics, 1943

[35] Toqeer M, Ali A, Alves TM, Khan A, Zubair, Hussain M. Application of model based post-stack inversion in the characterization of reservoir sands containing porous, tight and mixed facies: A case study from the Central Indus Basin, Pakistan. Journal of Earth System Science, 2021, 130(2): 1-21

[36] Veeken PCH, Da Silva M. Seismic inversion methods and some of their constraints. First Break, 2004, 22: 47-70

[37] Verma Y, Vishal V. Role of Geological Carbon-Dioxide sequestration in India’s efforts towards Net Zero Emissions. MGMI News Journal, 2021, 47: 40-49

[38] Verma Y, Vishal V. Evaluating CCS readiness in India: CO₂ storage potential, source-sink mapping and policy outlook. AGU Fall Meeting 2022, 2022

[39] Verma Y, Vishal V. Rahimpour MR, Makarem MA, Meshksar M, Bakhtyari A. Modeling and simulation of CO₂ geological storage. Advances and Technology Development in Greenhouse Gases: Emission, Capture and Conversion-Process Modelling and Simulation, 2024, 153-175

[40] Verma Y, Vishal V, Ranjith P. Risk analysis of injection-induced seismicity associated with geological CO₂ storage through enhanced oil recovery. AGU Fall Meeting 2021, 2021

[41] Verma Y, Vishal V, Ranjith PG. Sensitivity Analysis of Geomechanical Constraints in CO₂ Storage to Screen Potential Sites in Deep Saline Aquifers. Frontiers in Climate, 2021, 3: 1-22

[42] Vishal V. Saturation time dependency of liquid and supercritical CO₂ permeability of bituminous coals: Implications for carbon storage. Fuel, 2017, 192: 201-207

[43] Vishal V, Chandra D, Singh U, Verma Y. Understanding initial opportunities and key challenges for CCUS deployment in India at scale. Resour Conserv Recycl, 2021, 175: 105829

[44] Vishal V, Singh U, Bakshi T, Chandra D, Verma Y, Tiwari AK. Optimal source–sink matching and prospective hub–cluster configurations for CO₂ capture and storage in India. Geological Society, London, Special Publications, 2023, 528(1): 209-225

[45] Vishal V, Verma Y, Chandra D, Ashok D. A systematic capacity assessment and classification of geologic CO₂ storage systems in India. International Journal of Greenhouse Gas Control, 2021, 111: 103458

[46] Vishal V, Verma Y, Sulekh K, Singh TN, Dutta A. A firstorder estimation of underground hydrogen storage potential in Indian sedimentary basins. Geological Society, London, Special Publications, 2023, 528(1): 123-137

[47] Wang J, Wu J, Wu Z, Jeong J, Jeon G. Wiener filter-based wavelet domain denoising. Displays, 2017, 46: 37-41

[48] Xu G, Haq BU. Seismic facies analysis: Past, present and future. Earth Sci Rev, 2022, 224: 103876

[49] Yadav A, Yadav K, Sircar A. Feedforward neural network for joint inversion of geophysical data to identify geothermal sweet spots in Gandhar, Gujarat, India. Energy Geoscience, 2021, 2(3): 189-200

[50] Yilmaz Ö. Seismic Data Analysis, 2001, Tulsa, USA: Society of Exploration Geophysicists

[51] Zoback MD, Barton CA, Brudy M, Castillo DA, Finkbeiner T, Grollimund BR, Moos DB, Peska P, Ward CD, Wiprut DJ. Determination of stress orientation and magnitude in deep wells. International Journal of Rock Mechanics and Mining Sciences, 2003, 40(7–8): 1049-1076