Reservoir identification using full stack seismic inversion technique: A case study from Cambay basin oilfields, India

Chatterjee, Rima; Gupta, Soumyajit; Farroqui, M.Y.

doi:10.1016/j.petrol.2013.08.006

Cited by 15 publications

(7 citation statements)

References 9 publications

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“…The AI (acoustic impedance) property of the limestone reservoir rock of the Jaisalmer Formation was estimated based on P-wave velocity (derived from P-Sonic log) and density log data (Chatterjee et al 2013) (Chow et al 2005). The density log data were considered directly from…”

Section: Modelling Of the Well Log Data For Estimation Of The Elastic Propertiesmentioning

confidence: 99%

Capture the variation of acoustic impedance property in the Jaisalmer Formation due to structural deformation based on post-stack seismic inversion study: a case study from Jaisalmer sub-basin, India

Gupta

Sinha

Chahal

2022

J Petrol Explor Prod Technol

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The Rajasthan basin situates in the western part of India. The basin architecture comprises three significant sub-basins such as Barmer-Sanchor, Bikaner-Nagaur and Jaisalmer. Barmer-Sanchor and Bikaner-Nagaur sub-basins are intracratonic categories, whereas the Jaisalmer sub-basin comes under intracratonic nature. The current study was conducted in the Jaisalmer sub-basin. The study was conducted in two regions in the Jaisalmer sub-basin through a comparative quantitative interpretation study with the help of two vintages seismic surveys. Ghotaru and Bandha are two adjacent areas in the Jaisalmer sub-basin where Ghotaru has seen few hydrocarbon discoveries; however, no such discoveries are encountered in the Bandha area. The current study was concentrated on the Jaisalmer limestone formation in the Jurassic age. The sub-basin and its related study area have been structurally deformed due to various tectonic activities. Structural deformation was played a crucial role in changing the rock property of limestone facies. A post-stack seismic inversion was carried out to capture the rock property changes in the limestone reservoir based on P-impedance values. Development of high P-impedance was observed in the Ghotaru region compared to the Bandha region from this study. A frequency changes of the limestone lithofacies with structural components was also captured in this study. The high impedance limestone lithofacies is a probable hydrocarbon-bearing reservoir unit in the Jaisalmer Formation of the Ghotaru region.

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Section: Modelling Of the Well Log Data For Estimation Of The Elastic Propertiesmentioning

confidence: 99%

Capture the variation of acoustic impedance property in the Jaisalmer Formation due to structural deformation based on post-stack seismic inversion study: a case study from Jaisalmer sub-basin, India

Gupta

Sinha

Chahal

2022

J Petrol Explor Prod Technol

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“…Figure 20 shows the lithological and structural changes in between three study wells (Study_R1, Study_ST1 and Study_B1) through depth converted P-impedance volume. The acoustic impedance log was extracted as a property log from depth converted acoustic impedance volume along the well path of three study wells (Chatterjee et al 2013). This log was extracted due to comparative analysis with well based outcome.…”

Section: Fig 13mentioning

confidence: 99%

Capture the variation of the pore pressure with different geological age from seismic inversion study in the Jaisalmer sub-basin, India

Chahal

Gupta

2020

Pet. Sci.

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Geoscientific evidence shows that various parameters such as compaction, buoyancy effect, hydrocarbon maturation, gas effect and tectonic activities control the pore pressure of sub-surface geology. Spatially controlled geoscientific data in the tectonically active areas is significantly useful for robust estimation of pre-drill pore pressure. The reservoir which is tectonically complex and pore pressure is changing frequently that circumference motivated us to conduct this study. The changes in pore pressure have been captured from the fine-scale to the broad scale in the Jaisalmer sub-basin. Pore pressure variation has been distinctly observed in pre- and post-Jurassic age based on the current study. Post-stack seismic inversion study was conducted to capturing the variation of pore pressure. Analysis of low-frequency spectrum and integrated interval velocity model provided a detailed feature of pore pressure in each compartment of the study area. Pore pressure estimated from well log data was correlated with seismic inversion based result. Based on the current study one well has been proposed where pore pressure was estimated and two distinguished trends are identified in the study zone. The approaches of the current study were analysed thoroughly and it will be highly useful in complex reservoir condition where pore pressure varies frequently.

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“…Due to the complex conditions of geology, logging data often exhibit strong nonlinear relationships between them, and this nonlinear relationship may never be exactly obtained at the theoretical level (Ballin et al, 1992). Traditional forecasting methods have their own limitations for accurate prediction of reservoir parameters (Hamada, 2004;Chatterjee et al, 2013). The effects of longitudinal and transverse velocities on porosity and shale content are thought to have some correlation, but are difficult to predict quantitatively (Han et al, 1986).…”

Section: Introductionmentioning

confidence: 99%

Simultaneous prediction of multiple physical parameters using gated recurrent neural network: Porosity, water saturation, shale content

et al. 2022

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Reservoir parameter prediction is of significant value to oil and gas exploration and development. Artificial intelligence models are developing rapidly in reservoir parameter prediction. Unfortunately, current research has focused on multi-input single-output prediction models. Meaning, these models use a large amount of logging or seismic data to predict the petrophysical properties of a single reservoir. Another prominent problem is that most mechanistic learning studies have focused on using logging data (e.g., gamma ray and resistivity) to make predictions of reservoir parameters. Although these studies have yielded promising accuracy, a great shortcoming is the inability to obtain such data in logs by seismic inversion. The value of our research work is to achieve a complete description of the reservoir using the elastic parameters from the seismic inversion. We developed a deep learning method based on gated recurrent neural network (GRNN) suitable for simultaneous prediction of porosity, saturation and shale content in the reservoir. GRNN is based on Gated Recurrent Unit (GRU), which can automatically update and reset the hidden state. The input parameters to the model are compressive wave velocity, shear wave velocity and density. The model is trained to fit nonlinear relationships between input parameters and multiple physical parameters. We employed two wells: one for testing and the other for training. 20% of the data in the training wells were used as the validation set. In preprocessing, we performed z-score whitening on the input data. During the training phase, the model hyperparameters were optimized based on the mean absolute error (MAE) box plots of the validation set. Experiments on the test data show that the model has superior robustness and accuracy compared to the conventional recurrent neural network (RNN). In the GRNN prediction results of the test set, the MAE is 0.4889 and the mean squared error (MSE) is 0.5283. Due to the difference in input parameters, our prediction is weaker than the research method using logging data. However, our proposed method has higher practical value in exploration work.

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Reservoir identification using full stack seismic inversion technique: A case study from Cambay basin oilfields, India

Cited by 15 publications

References 9 publications

Capture the variation of acoustic impedance property in the Jaisalmer Formation due to structural deformation based on post-stack seismic inversion study: a case study from Jaisalmer sub-basin, India

Capture the variation of acoustic impedance property in the Jaisalmer Formation due to structural deformation based on post-stack seismic inversion study: a case study from Jaisalmer sub-basin, India

Capture the variation of the pore pressure with different geological age from seismic inversion study in the Jaisalmer sub-basin, India

Simultaneous prediction of multiple physical parameters using gated recurrent neural network: Porosity, water saturation, shale content

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