Multi-point geostatistics – an introductory overview

Daly, Colin; Caers, Jef

doi:10.3997/1365-2397.2010020

Cited by 41 publications

(15 citation statements)

References 24 publications

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“…Thus, the definition of a correct velocity model to minimize the uncertainty of the time-todepth conversion has a significant impact on the accuracy of the stratigraphic inversion. If the uncertainties associated with the initial and boundary conditions of basin-scale simulations can be significantly reduced, the lithostratigraphic record generated by the sub-grid parameterization can be used to predict the spatial distribution of lithology at the reservoir scale through application of appropriate geostatistical postprocessing tools such as multipoint statistics (Daly and Caers, 2010). This will be the subject of a follow-up study.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Towards process-based geological reservoir modelling: Obtaining basin-scale constraints from seismic and well data

Sacchi

Weltje

Verga

2015

Marine and Petroleum Geology

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Highlights:• Fluvio-deltaic stratigraphy was simulated with a 2DH process-based model • Goodness of fit functions were used to infer boundary conditions from subsurface data • Information content of seismic and well data was evaluated • Depth of reservoir top across basin is best predictor of reservoir lithology ABSTRACT Forward stratigraphic modelling aims at representing the spatial distribution of lithology as a function of physical processes and environmental conditions at the time of deposition so as to integrate geological knowledge into the reservoir modelling workflow, thus increasing predictive capabilities of reservoir models and efficient exploitation of hydrocarbons. Application of process-based models in inverse mode is not yet well-established due to our limited insight into the information content of common subsurface data and the computational overhead involved.In this paper we examine inverse modelling of stratigraphy by using a typical dataset acquired in the hydrocarbon industry, which consists of seismic data and standard logs from a limited number of wells. The approach is based on the use of a forward model called SimClast, developed at Delft University of Technology, to generate facies distribution and architecture at the regional scale. Three different goodness of fit functions were proposed for model inversion, following an inference approach. A synthetic reservoir unit was used to investigate the impact of the uncertainty affecting the input parameters and the information content of seismic and well data.The case study showed that the model was more sensitive to the initial topography and to the location of the sediment entry point than to sea level. The depth of the seismic reflector corresponding to the top-reservoir surface was the most informative data source; the initial and boundary conditions of the simulation were constrained by evaluating the depth of this reflector across the whole basin area. In the reservoir area, where the seismic-to-well tie was established, the depth of the reservoir top does not give enough information for constraining the model parameters. Our results thus indicate that evaluation of basinscale data permits reduction of uncertainty in (geostatistical) reservoir models relative to the current workflow, in which only local data are used. Effective use of well data to generate reservoir models conditioned to basin-scale scenarios requires post-processing methods to downscale the output of the forward model used in the experiments.

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Section: Accepted Manuscriptmentioning

confidence: 99%

Towards process-based geological reservoir modelling: Obtaining basin-scale constraints from seismic and well data

Sacchi

Weltje

Verga

2015

Marine and Petroleum Geology

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“…Geostatistical modeling approaches such as multiple-point geostatistical methods (Daly and Caers, 2010;Strebelle, 2002), transition probability indicator simulation (Carle and Fogg, 1996), or sequential indicator simulation (Deutsch and Journel, 1998) provide models with a higher degree of objectivity in shorter time compared to the cognitive, manual modeling approaches. An example of combining AEM and borehole information in a transition probability indicator simulation approach is given by He et al (2014).…”

Section: N Foged Et Almentioning

confidence: 99%

Large-scale 3-D modeling by integration of resistivity models and borehole data through inversion

Foged

Marker

Christansen

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. We present an automatic method for parameterization of a 3-D model of the subsurface, integrating lithological information from boreholes with resistivity models through an inverse optimization, with the objective of further detailing of geological models, or as direct input into groundwater models. The parameter of interest is the clay fraction, expressed as the relative length of clay units in a depth interval. The clay fraction is obtained from lithological logs and the clay fraction from the resistivity is obtained by establishing a simple petrophysical relationship, a translator function, between resistivity and the clay fraction. Through inversion we use the lithological data and the resistivity data to determine the optimum spatially distributed translator function. Applying the translator function we get a 3-D clay fraction model, which holds information from the resistivity data set and the borehole data set in one variable. Finally, we use kmeans clustering to generate a 3-D model of the subsurface structures. We apply the procedure to the Norsminde survey in Denmark, integrating approximately 700 boreholes and more than 100 000 resistivity models from an airborne survey in the parameterization of the 3-D model covering 156 km 2 . The final five-cluster 3-D model differentiates between clay materials and different high-resistivity materials from information held in the resistivity model and borehole observations, respectively.

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“…A range of geostatisticalsimulation approaches includes object-based modelling, variograms, kriging and co-kriging, covariance functions, or multipoint statistics. A detailed review of the advantages and disadvantages of these methods is beyond the scope of this paper but see works by authors including Deutsch & Journel (1998), Jensen et al (2000), Deutsch (2002) and Daly & Caers (2010). Geostatistical modelling of reservoir properties is complemented by process-based models that simulate the sedimentation and compaction of carbonate rocks.…”

Section: Background and Challengesmentioning

confidence: 99%

Fundamental controls on fluid flow in carbonates: current workflows to emerging technologies

Agar

Geiger

2014

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The introduction reviews topics relevant to the fundamental controls on fluid flow in carbonate reservoirs and to the prediction of reservoir performance. The review provides research and industry contexts for papers in this volume only. A discussion of global context and frameworks emphasizes the value yet to be captured from compare and contrast studies. Multidisciplinary efforts highlight the importance of greater integration of sedimentology, diagenesis and structural geology. Developments in analytical and experimental methods, stimulated by advances in the materials sciences, support new insights into fundamental (pore-scale) processes in carbonate rocks. Subsurface imaging methods relevant to the delineation of heterogeneities in carbonates highlight techniques that serve to decrease the gap between seismically resolvable features and well-scale measurements. Methods to fuse geological information across scales are advancing through multiscale integration and proxies. A surge in computational power over the last two decades has been accompanied by developments in computational methods and algorithms. Developments related to visualization and data interaction support stronger geoscience-engineering collaborations. High-resolution and real-time monitoring of the subsurface are driving novel sensing capabilities and growing interest in data mining and analytics. Together, these offer an exciting opportunity to learn more about the fundamental fluid-flow processes in carbonate reservoirs at the interwell scale.

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Multi-point geostatistics – an introductory overview

Cited by 41 publications

References 24 publications

Towards process-based geological reservoir modelling: Obtaining basin-scale constraints from seismic and well data

Towards process-based geological reservoir modelling: Obtaining basin-scale constraints from seismic and well data

Large-scale 3-D modeling by integration of resistivity models and borehole data through inversion

Fundamental controls on fluid flow in carbonates: current workflows to emerging technologies

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