An integrated geophysical study for the assessment and monitoring of CO2 sequestration in Gandhar Oilfield, Cambay Basin, India

Zia, Saqib; White, Jim; Vosper, Hayley; Vedanti, Nimisha. 2024 An integrated geophysical study for the assessment and monitoring of CO2 sequestration in Gandhar Oilfield, Cambay Basin, India. [Lecture] In: Society of Exploration Geophysicists. Role of Geosciences in Carbon Storage., Mumbai, India, 19-21 Mar 2024. (Unpublished)

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Introduction Gandhar oilfield, Cambay Basin, Gujarat is one of the Oil and Natural Gas Corporation Limited's (ONGC) major brownfields and a pilot candidate for India’s first large-scale CO2 sequestration project. The field which has been produced from multilayered sand bodies of Hazad Member, Ankleshwar formation has undergone various phases of production, and currently, it has reached a highly matured stage of its production life. ONGC has planned to implement the CO2 EOR technique in this field to recover an extra 15% of residual oil based on recent studies of source-sink matching, petrophysical properties analysis, current reservoir pressure, minimum miscibility pressure (MMP), and other laboratory experiments. The field is being studied for the assessment of CO2 storage potential and the feasibility of seismic techniques to monitor injected CO2 in Hazad sands (reservoir) and overlying brine saturated Ardol sands of Ankleshwar formation. For such a study, an integrated geophysical approach is performed with seismic, petrophysical, and geophysical well-log data provided by the National Data Repository-DGH (Govt. of India) and ONGC. Prediction of unrecorded P-sonic logs using Gradient Boosting Regressor algorithm in Gandhar oilfield A total of 15 wireline geophysical well log data from a 50 sq. km. block of the Gandhar oilfield for this study were provided but only 12 out of the 15 wells had P)-sonic logs. The sonic log (DT) is one of the essential logs required to perform petrophysical analysis, to aid in missing check-shot profiling, fluid substitution modeling, and seismic modeling & inversion studies. Therefore, the prediction of unrecorded P-sonic logs was required to reduce the uncertainty in reservoir characterization at the field scale by reducing the scarcity of sonic log data over an area. To overcome this problem, we implemented the Ensemble machine learning technique named Gradient Boosting Regressor (GBR) (J.H. Friedman, 2001, 2002) algorithm to predict the unrecorded P-sonic logs in the Gandhar oilfield. The GBR has unique functionality to reduce bias and variance problems by converting weak learners to strong ones. Also, GBR can optimize different loss functions and provides several hyperparameter tuning options that make the prediction function fit very flexibly. The predicted P-sonic log correlates very well with the original P-sonic log. The predicted sonic logs are then used in the geomodel building process and fluid substitution modeling for CO2 sequestration. Gandhar Oilfield Geomodel Building We interpreted the 3D seismic data of the Gandhar oilfield with the help of the geophysical well logs including the predicted one and prepared a structural and stratigraphic geomodel of the Gandhar oilfield. The geomodel consists of 12 sand layer units (multi-stratigraphic pay sands) with thin intercalated shales in between them. The shales are as thin as 1.5 m and incorporating them in the geomodel was a hectic task that required several updates (well-tie tomography) in the building process to reduce mistie to avoid the crosscut between layers. We first displayed the prepared geomodel on 3D seismic data and it is observed that it honors the seismic data very well. The geomodel was flooded with facies interpreted from the geophysical well logs, effective porosity, and water saturation for the purpose of CO2 storage capacity assessment. Synthetic seismogram generation for monitoring CO2 sequestration in Gandhar oilfield The feasibility of seismic data to monitor CO2 sequestration in the overburden of the reservoir, which is brine saturated Ardol sands of the Ankleshwar Formation, is demonstrated through convolutional synthetic seismic modeling and seismograms. The evolution of an expanding CO2 plume in Ardol sands was calculated through the analytical approximation of axisymmetric gravity currents in a brine-CO2-saturated medium by using an injection rate of ∼0.5 Mt/year for a period of 6 years. CO2-saturated rock properties were determined using Gassmann fluid substitution and monitoring of CO2 plume was imaged by a time-lapse convolutional synthetic seismogram. Random noises were added to the synthetic seismogram and then NRMS and repeatability metrics were performed which established the detection threshold of 30 days since CO2 injection. The results are shown in Figure 1. Now, we don’t have the geomodel ready for the full-wavefield seismic modeling as it is in the development process. The Poroviscoelastic wave equation was solved numerically and was tested on the Sleipner field geomodel for monitoring the CO2 sequestration. Full-wavefield synthetic seismograms were generated for the pre-and post-CO2 injection cases. The poroviscoelastic theory models realistic amplitude attenuated due to squirt-flow (viscoelasticity) and Biot-flow (poroelasticity) related to matrix fluid coupling relaxation mechanisms. The prediction of more realistic amplitudes subject to CO2 sequestration signifies the success of the poroviscoelastic theory in monitoring CO2 sequestration in geological formations. Conclusions Gradient Boosting (ML) can be used for the prediction of logs, and it clearly demarcates the lithological boundary changes in Gandhar by preserving the amplitude. Fine scale geomodel of Gandhar oilfield (Hazad sands, Ankleshwar Formation) will give more accurate estimation of CO2 storage capacity. We anticipate that monitoring of CO2 sequestration on synthetic seismogram by poroviscoelastic theory will be more enhanced due to realistic amplitude attenuation attributed to interplay of squirt-flow and Biot-flow relaxation mechanisms. In addition to Hazad Sands (reservoir), brine saturated Ardol Sands is studied for the possibility of CO2 sequestration. The detection threshold is obtained for 30 days since CO2 injection in Ardol sands.

Item Type: Publication - Conference Item (Lecture)
Additional Keywords: IGRD
Date made live: 03 Apr 2024 14:36 +0 (UTC)

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