Estimation of petrophysical parameters using seismic inversion and neural network modeling in Upper Assam basin, India

Gogoi, Triveni; Chatterjee, Rima

doi:10.1016/j.gsf.2018.07.002

Cited by 81 publications

(16 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the most important seismic attributes is the acoustic impedance, from which a porosity model can be derived (Gogoi and Chatterjee 2019). Acoustic impedance can be calculated by seismic amplitude inversion, because the amplitudes of the reflected waves change, depending on the impedance contrast at an interface.…”

Section: Introductionmentioning

confidence: 99%

Porosity estimation of a geothermal carbonate reservoir in the German Molasse Basin based on seismic amplitude inversion

Wadas

Hartmann

2022

Geotherm Energy

View full text Add to dashboard Cite

The Molasse Basin is one of the most promising areas for deep geothermal exploitation in Germany and the target horizon is the aquifer in the Upper Jurassic carbonates. Carbonate deposits can be very heterogeneous even over a small area due to diagenetic processes and varying depositional environments. The preferential targets for geothermal exploitation in carbonate deposits are fault zones, reef facies and karstified areas, since they are expected to act as hydraulically permeable zones due to high porosity and high permeability. Therefore, identifying these structures and characterizing, e.g., their internal porosity distribution are of high importance. This can be accomplished using 3D reflection seismic data. Besides structural information, 3D seismic surveys provide important reservoir properties, such as acoustic impedance, from which a porosity model can be derived. In our study area in Munich we carried out a seismic amplitude inversion to get an acoustic impedance model of the Upper Jurassic carbonate reservoir using a 3D seismic data set, a corresponding structural geological model, and logging data from six wells at the ‘Schäftlarnstraße’ geothermal site. The impedance model and porosity logs were than used to calculate a porosity model. The model shows a wide porosity range from 0 to 20% for the entire reservoir zone and the lithology along the wells reveals that dolomitic limestone has the highest porosities and calcareous dolomite has the lowest porosities. The study area is cut by a large W–E striking fault, the Munich Fault, and the footwall north of it shows higher porosities and more intense karstification than the hanging wall to the south. Considering the entire study area, an increase in porosity from east to west is observed. Furthermore, we identified a complex porosity distribution in reef buildups and pinnacle reefs. The reef cores have mostly low porosities of, e.g., < 3% and the highest porosities of up to 7 to 14% are observed at the reef caps and on the reef slopes. The reef slopes show a characteristic interfingering of the reef facies with the surrounding bedded facies, which indicates a syn-sedimentary reef development with slightly varying build up growth rates. We also assessed the reservoir quality with regard to porosity distribution and determined areas with moderate to good quality for geothermal exploitation by defining porosity evaluation levels. The porosity evaluation maps show that the carbonate rocks of Berriasian to Malm $$\zeta$$ ζ 1 are preferential targets for exploitation, especially in the footwall of the Munich Fault and to the west of the hanging wall, because these areas are characterized by high porosities due to intense karstification of bedded and massive facies, although the latter is mainly restricted to reef caps and reef slopes.

show abstract

Section: Introductionmentioning

confidence: 99%

Porosity estimation of a geothermal carbonate reservoir in the German Molasse Basin based on seismic amplitude inversion

Wadas

Hartmann

2022

Geotherm Energy

View full text Add to dashboard Cite

show abstract

“…Several case studies using core, seismic, and wireline log data have been effectively applied in reservoir characterization to explore and develop hydrocarbon (Das and Chatterjee 2018;Gogoi and Chatterjee 2019;Gupta et al 2012;Nabawy et al 2018;Neely et al 2021;Sohail et al 2020;Teama et al 2019;Verwer and Braaksma 2009). An enhanced classification of lithofacies and depositional environment centred on core, well log, and seismic data is important for formation evaluation, prospect identification and field development (Ashraf et al 2019;Elamri and Opuwari 2016;Evans et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…An enhanced classification of lithofacies and depositional environment centred on core, well log, and seismic data is important for formation evaluation, prospect identification and field development (Ashraf et al 2019;Elamri and Opuwari 2016;Evans et al 2019). The heterogeneity and the complex nature of reservoir properties can be effectively described and quantified by means of available core data, well log and seismic data (Gogoi and Chatterjee 2019).…”

Section: Introductionmentioning

confidence: 99%

An integrated approach of reservoir characterization of Y gas field in Central Bredasdorp Basin, South Africa

Masindi

(1)²,

Opuwari

2022

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

The study attempts to address the knowledge gap in the Bredasdorp Basin Offshore South Africa by using newly acquired seismic data with an enhanced resolution, integrating core and well log to provide a solution. The main objective of this study was a volumetric sandstone reservoir characterization of the 13At1 and 10At1 sandstones deposited in the upper shallow marine environment. The results reveal four facies grouped as facies 1(claystone), facies 2(intercalation of claystone and sandstone, facies 3(medium sandstone), and facies 4 fine-medium sandstone grain deposited in a deep marine environment. Facies 3, the medium-grained sandstone, has the best reservoir quality rock, while facies 1, which is predominantly claystone, has the least rock quality. The study has produced a calculated volume of gas in the 10At1 sand (upper and lower sand is 30.01 billion cubic feet (bcf)) higher than that of the 13At1 sand (27.22 bcf of gas). Improved seismic resolution enhanced the accuracy of results. It is advisable to focus further field development in the eastern part of the Y gas field because of good reservoir quality and production developed due to the sand in the 10At1 sequence.

show abstract

“…Moreover, integrating various techniques showed effectiveness in reservoir characterisation. Such approaches tend to reduce uncertainties and enhance reservoir properties' prediction [6,7].…”

Section: Introductionmentioning

confidence: 99%

Reservoir Characterisation of High-Pressure, High-Temperature Zone of Malay Basin Using Seismic Inversion and Artificial Neural Network Approach

et al. 2021

View full text Add to dashboard Cite

The growing demand for hydrocarbons has driven the exploration of riskier prospects in depths, pressures, and temperatures. Substantial volumes of hydrocarbons lie within deep formations, classified as high pressure, high temperature (HPHT) zone. This study aims to delineate hydrocarbon potential in the HPHT zone of the Malay Basin through the integrated application of rock physics analysis, pre-stack seismic inversion, and artificial neural network (ANN). The zones of interest lie within Sepat Field, located offshore Peninsular Malaysia, focusing on the HPHT area in Group H. The rock physics technique involves the cross-plotting of rock properties, which helps to differentiate the lithology of sand and shale and discriminates the fluid into water and hydrocarbon. The P-impedance, S-impedance, Vp/Vs ratio, density, scaled inverse quality factor of P (SQp), and scaled inverse quality factor of S (SQs) volumes are generated from pre-stack seismic inversion of 3D seismic data. The obtained volumes demonstrate spatial variations of values within the zone of interest, indicating hydrocarbon accumulation. Furthermore, the ANN model is successfully trained, tested, and validated using 3D elastic properties as input, to predict porosity volume. Finally, the trained neural network is applied to the entire reservoir volume to attain a 3D porosity model. The results reveal that rock physics study, pre-stack seismic inversion, and ANN approach helps to recognize reservoir rock and fluids in the HPHT zone.

show abstract

Estimation of petrophysical parameters using seismic inversion and neural network modeling in Upper Assam basin, India

Cited by 81 publications

References 21 publications

Porosity estimation of a geothermal carbonate reservoir in the German Molasse Basin based on seismic amplitude inversion

Porosity estimation of a geothermal carbonate reservoir in the German Molasse Basin based on seismic amplitude inversion

An integrated approach of reservoir characterization of Y gas field in Central Bredasdorp Basin, South Africa

Reservoir Characterisation of High-Pressure, High-Temperature Zone of Malay Basin Using Seismic Inversion and Artificial Neural Network Approach

Contact Info

Product

Resources

About