Reservoir Model Design: A Practitioner's Guide by Philip Ringrose, Mark Bentley

By Philip Ringrose, Mark Bentley

This publication supplies sensible suggestion and able to use tips about the layout and development of subsurface reservoir types. The layout parts conceal rock structure, petrophysical estate modelling, multi-scale information integration, upscaling and uncertainty research. Philip Ringrose and Mark Bentley proportion their adventure, received from over 100 reservoir modelling reviews in 25 nations masking clastic, carbonate and fractured reservoir kinds. The intimate courting among geology and fluid stream is explored all through, displaying how the impression of fluid style, creation mechanism and the subtleties of unmarried- and multi-phase movement mix to persuade reservoir version design.

The major viewers for this publication is the neighborhood of utilized geoscientists and engineers fascinated with the advance and use of subsurface fluid assets. The booklet is appropriate for a variety of Master’s point classes in reservoir characterisation, modelling and engineering.

· presents sensible recommendation and directions for clients of 3D reservoir modelling packages

· offers suggestion on reservoir version layout for the growing to be world-wide task in subsurface reservoir modelling

· Covers rock modelling, estate modelling, upscaling and uncertainty dealing with

· Encompasses clastic, carbonate and fractured reservoirs

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Upper image: AI volume rendered into cells. Lower image: Best reservoir properties (red, yellow) preferentially guided by high AI values (Image courtesy of Simon Smith) framework model log & core-scale detail improved seismic amplitude interpretation Subsurface concept reservoir model content seismic frequency scale of heterogeneity Outcrop analogue Fig. 21 Seismic conditioning: deterministic and probabilistic elements of a reservoir model in the context of frequency & scale versus amplitude & content 34 The plot is a convenient backdrop for arranging the components of a reservoir model, and the frequency/amplitude axes can be alternatively labelled for ‘reservoir model scale’ and ‘content’.

6 Essential Geostatistics 41 Pixel column 28 g Pixel column 54 Pixel column 8 lag g g lag lag Fig. 29 Semivariograms for pixel pairs on selected N-S transects 5000 4500 4000 variance 3500 3000 2500 2000 1500 Range – 22 pixels 1000 500 0 0 10 20 30 lag (pixels) Fig. 30 Semivariogram based on all E-W transects 40 50 60 42 2 The Rock Model 6000 5000 variance 4000 3000 2000 Range – 32 pixels 1000 0 0 10 20 30 40 50 60 lag (pixels) Fig. 31 Semivariogram based on all N-S transects 4. Estimate appropriate variogram ranges for individual elements (with different variogram ranges for the horizontal and vertical directions); 5.

E. 6 Essential Geostatistics 37 Nugget model Gaussian model 1 1 g g 0 0 distance (lag) distance (lag) Spherical model Power law model 1 1 g g 0 0 distance (lag) distance (lag) Exponential model Hole model 1 1 g g 0 0 distance (lag) distance (lag) Fig. com)) spatially more heterogeneous. The presence of a nugget means that although the dataset displays correlation, quite sudden variations between neighbouring points can occur, such as when gold miners come across a nugget, hence the name. The nugget is also related to the sample scale – an indication that there is variation at a scale smaller than the scale of the measurement.

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