Integration of geology and geophysics through geostatistical inversion: a case study

Sams, Mark; Millar, I.; Satriawan, Wawan; Saussus, D.; Bhattacharyya, Sumon

doi:10.3997/1365-2397.2011023

Cited by 21 publications

(7 citation statements)

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“…This is hardly true for complex geological environments that are highly non-stationary. The issue of non-stationarity has been tackled with different approaches and frameworks (Rimstad and Omre 2009;Sams et al 2011). The nonstationary behaviour of a given petro-elastic property of interest may be related to different medium-to large-scale geologic units.…”

Section: Global Stochastic Inversionmentioning

confidence: 99%

Geostatistical seismic inversion for non‐stationary patterns using direct sequential simulation and co‐simulation

Sabeti

Moradzadeh

Ardejani

et al. 2017

Geophysical Prospecting

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Geostatistical seismic inversion methods are routinely used in reservoir characterisation studies because of their potential to infer the spatial distribution of the petro‐elastic properties of interest (e.g., density, elastic, and acoustic impedance) along with the associated spatial uncertainty. Within the geostatistical seismic inversion framework, the retrieved inverse elastic models are conditioned by a global probability distribution function and a global spatial continuity model as estimated from the available well‐log data for the entire inversion grid. However, the spatial distribution of the real subsurface elastic properties is complex, heterogeneous, and, in many cases, non‐stationary since they directly depend on the subsurface geology, i.e., the spatial distribution of the facies of interest. In these complex geological settings, the application of a single distribution function and a spatial continuity model is not enough to properly model the natural variability of the elastic properties of interest. In this study, we propose a three‐dimensional geostatistical inversion technique that is able to incorporate the reservoir's heterogeneities. This method uses a traditional geostatistical seismic inversion conditioned by local multi‐distribution functions and spatial continuity models under non‐stationary conditions. The procedure of the proposed methodology is based on a zonation criterion along the vertical direction of the reservoir grid. Each zone can be defined by conventional seismic interpretation, with the identification of the main seismic units and significant variations of seismic amplitudes. The proposed method was applied to a highly non‐stationary synthetic seismic dataset with different levels of noise. The results of this work clearly show the advantages of the proposed method against conventional geostatistical seismic inversion procedures. It is important to highlight the impact of this technique in terms of higher convergence between real and inverted reflection seismic data and the more realistic approximation towards the real subsurface geology comparing with traditional techniques.

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Section: Global Stochastic Inversionmentioning

confidence: 99%

Geostatistical seismic inversion for non‐stationary patterns using direct sequential simulation and co‐simulation

Sabeti

Moradzadeh

Ardejani

et al. 2017

Geophysical Prospecting

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“…The variogram estimation was established by fitting modelled variogram on the computed experimental variogram. For modelling discrete property, facies probability trend (Constant proportion, 1D, 2D or 3D trend model) can be utilizied to guide the geostatistical inversion [Sams 2011]. In this study, 2D facies probability trend map was constructed for each of the layer.…”

Section: Figure 6 -The Top Picture Shows the Overlaid Of Measured (Blmentioning

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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“…and inverted P-elastic impedance (Dubucq and Van Riel 2001;Chatterjee et al 2013;Vernik et al 2002) or through rock physics templates Avseth et al (2005). To deal with the ill-posed problem of deterministic inversion and also the presence of thin bed reservoirs, some authors prefer the stochastic inversion approach (Bachrach 2006;Doyen 2009;Sams et al 2011;Sengupta and Bachrach 2007) and the nonlinear inversion approach (Morteza Amiri et al 2015;Romero and Carter 2001). In the complex stacked channel sequence-Oligocene XB3-discrimination of reservoir sand from the nonreservoir formations is an awkward exercise provided both lithologies show nearly the same values of acoustic impedance.…”

Section: Introductionmentioning

confidence: 99%

Reservoir properties prediction using extended elastic impedance: the case of Nianga field of West African Congo basin

Samba

Lü

Habib

2017

J Petrol Explor Prod Technol

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Simultaneous seismic inversion and extended elastic impedance (EEI) were applied to obtain quantitative estimates of porosity, water saturation, and shale volume over Nianga field of Congo basin, West Africa. The optimum angle at which EEI log and the target petrophysical parameter give the maximum correlation was meticulously analyzed by additionally incorporating the concept of relative rock physics. Prestack seismic data were simultaneously inverted into Vp, acoustic, and gradient impedances. The last two broadband inverted volumes were projected to Chi angles corresponding to the target petrophysical parameters, and three broadband EEI volumes were obtained. At well control points, the linear trends based on specific lithology between EEI and petrophysical parameters were then used to transform EEI volumes into quantitative porosity, water saturation, and shale volume cubes. In order to obtain the reservoir facies distribution, another concept of minimum energy angle was used to generate the background EEI cube, thereby enabling the mapping of reservoir facies. From quantitative porosity, water saturation, shale content, and background EEI cubes, favorable zones have been pinpointed which may suggest possible drilling locations for future development of the field.

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Integration of geology and geophysics through geostatistical inversion: a case study

Cited by 21 publications

References 0 publications

Geostatistical seismic inversion for non‐stationary patterns using direct sequential simulation and co‐simulation

Geostatistical seismic inversion for non‐stationary patterns using direct sequential simulation and co‐simulation

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

Reservoir properties prediction using extended elastic impedance: the case of Nianga field of West African Congo basin

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