Joint stochastic inversion of 3D prestack seismic-amplitude data and well logs for high-resolution reservoir characterization of deepwater hydrocarbon reservoirs

Contreras, Arturo; Torres‐Verdín, Carlos; Fasnacht, Timothy; Chesters, William; Kvien, Knut

doi:10.1190/tle33050520.1

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…The geostatistical inversion algorithm uses a combination of Bayesian probabilistic inference and Markov Chain Monte Carlo [Contreras 2014] which is a class of iterative methods for sampling from a probability density function. Bayesian probabilistic inference is used to create a fusion of the different probability density functions that represent the different sources of prior information and measurements (evidence data) that are of different scales.…”

Section: Methodsmentioning

confidence: 99%

Integrating Seismic and Well Data into Highly Detailed Reservoir Model through AVA Geostatistical Inversion

Sulistiono

Vaughan²,

Ali³

et al. 2015

Day 3 Wed, November 11, 2015

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Building a reservoir model by integrating seismic angle stacks and well data has been challenging for oil and gas companies. Seismic data contain very detailed information of reservoir properties lateraly but lack vertical resolution while well data provide very detailed information vertically but lack horizontal information. One established method of integrating these two kinds of data is deterministic seismic inversion. Deterministic seismic inversion has proven to be a good method in delineating the reservoirs at seismic resolution but it has limitation in analyzing thin reservoirs and it only pruduced one result. Geostatistical seismic inversion method has the ability to integrate many types of data and produce multiple relaizations of the results. The realizations are at much higher details than seismic data and are able to capture thin reservoirs.An AVA geostatistical seismic inversion workflow was successfully implemented to produce highly detailed reservoir models of Abu Madi reservoir sands in Nile delta, Egypt. Abu Madi Formation is composed of lacustrine turbidite deposits in semi-isolated basin and can be subdivided into Upper and Lower reservoirs. Intraformational shale baffles occur quite commonly within Abu Madi Formation which acted as barriers of pressure depletion between the Upper and Lower reservoirs. Well data analysis has demonstrated the complex pattern of the stacked reservoir sand zones of heterogeneous reservoir parameters and pressure trends. The main objective of the study was to produce reservoir models that could be used to understand the observed pressure depletion trends within the Upper and Lower Abu Madi reservoirs which have great significance for effective field management.Seismic angle stacks and well data were integrated through AVA geostatistical inversion to produce highly detailed lithotype and elastic property results at 0.5 ms vertical sampling. Thesse models have successfully captured the shale baffles. Lithotype and elastic property realizations were used to cosimulate for reservoir (engineering) properties of Effective Porosity and Volume of Clay. The Effective Porosity and Volume of Clay realizations were then ranked to provide the P10, P50 and P90 models to be used as input for dynamic flow simulation. Five permeability rock types were derived based on extensive SCAL database and used to define the permeability and saturation models. These detailed engineering models were used for dynamic flow simulation and successfully predicted the pressure depletion trends in the Abu Madi reservoirs.

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Section: Methodsmentioning

confidence: 99%

Integrating Seismic and Well Data into Highly Detailed Reservoir Model through AVA Geostatistical Inversion

Sulistiono

Vaughan²,

Ali³

et al. 2015

Day 3 Wed, November 11, 2015

View full text Add to dashboard Cite

show abstract

FMG_INV, a Fast Multi-Gaussian Inversion Method Integrating Well-Log and Seismic Data

Shi,

Yu,

Zhou

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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The Application of Very Large Numbers of Pseudo-Wells for Reservoir Characterisation

Connolly¹,

Hughes²

2014

Day 1 Mon, November 10, 2014

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We describe a method to invert seismic data by matching to large numbers of pseudo-wells. Pseudo-well construction is based on lithofacies micro-layering constrained by a model for the vertical statistics. Elastic properties are derived from a rock physics model. We match either to single or multiple angle-stacks using synthetics based on extended elastic impedance. This is a joint inversion process as it simultaneously accounts for both elastic parameters and reservoir properties. Both localised means and standard deviations of reservoir properties are estimated. We do not include priors for lateral statistics but instead we envisage this process forming the first step of a two stage workflow with the output providing constrains for geostatistical modelling in the second stage. With no constraints on lateral correlation the first stage can operate in 1D greatly decreasing computational requirements. We show examples of application to two fields; one offshore Angola and the other offshore West of Shetlands. These demonstrate the quality of the algorithm which provides laterally stable results, high signal-to-noise modelling of the seismic and accurate blind well predictions.

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Joint stochastic inversion of 3D prestack seismic-amplitude data and well logs for high-resolution reservoir characterization of deepwater hydrocarbon reservoirs

Cited by 5 publications

References 6 publications

Integrating Seismic and Well Data into Highly Detailed Reservoir Model through AVA Geostatistical Inversion

Integrating Seismic and Well Data into Highly Detailed Reservoir Model through AVA Geostatistical Inversion

FMG_INV, a Fast Multi-Gaussian Inversion Method Integrating Well-Log and Seismic Data

The Application of Very Large Numbers of Pseudo-Wells for Reservoir Characterisation

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