Sensitivity of Oil Production to Petrophysical Heterogeneities

Skorstad, A.; Kolbjørnsen, Odd; Fjellvoll, Bjørn; Howell, John; Manzocchi, Tom; Carter, Jonathan N.

doi:10.1007/978-1-4020-3610-1_73

Cited by 9 publications

(2 citation statements)

References 7 publications

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“…Although the reservoir is synthetic, it is important to incorporate realistic behavior in it. The synthetic reservoir is based on two synthetic reservoirs from the European Union research project SAIGUP (Skorstad et al, 2005). Here, considerable effort was made in generating realistic synthetic reservoirs based on observed features in both recent and ancient shallow marine systems.…”

Section: Synthetic Reservoirmentioning

confidence: 99%

Information Content in Forward 4D-Seismic Modelling and Elastic Inversion

et al. 2005

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractElastic seismic inversion is a tool frequently used in analysis of seismic data. Elastic inversion relies on a simplified seismic model, and generally produces 3D cubes for Vp, Vs and density. By applying rock physics theory such volumes may be interpreted in terms of lithology and fluid properties. Understanding the robustness of forward and inverse techniques is important when deciding how much information seismic data really carry.This paper discusses the observed deviation between a reference and simulated reservoir, and its dependency on the seismic parameters and the reservoir characterization parameters. The ability to utilize the results from a 4D seismic survey in reservoir characterization will depend on several aspects. To investigate this, a loop that performs independent forward seismic modeling and elastic inversion at two time stages has been established.The multi-disciplinary workflow has several independent steps:1. Generation of a synthetic reference reservoir, by realistic geostatistical modeling. 2. Flow simulation of the reference reservoir to predict reservoir conditions at survey acquisition times. 3. Establishing a relationship between petrophysical and fluid properties and seismic parameters by a rock physics model. 4. Generation of seismic AVA responses corresponding to reservoir conditions at base and monitor survey times. 5. Elastic seismic inversion of both AVA response sets. 6. Simulation of lithology and fluid parameters conditioned on seismic inversion. 7. Comparison of static reservoir parameters of reference and simulated realization.8. Comparison of seismic responses at initial and monitor survey times. By working on a realistic synthetic reservoir, full knowledge of the reservoir characteristics is achieved. This makes the evaluation of the questions regarding the fundamental dependency between the seismic and petrophysical domains stronger. The theoretical limitations of the information content of the seismic data, including 4D, are investigated since the synthetic reservoir is an ideal case with accuracy never achieved in the applied situation.The production deviation between the reference and predicted reservoir was significantly decreased by using 4D seismic data in addition to the 3D inverted elastic parameters.

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Section: Synthetic Reservoirmentioning

confidence: 99%

Information Content in Forward 4D-Seismic Modelling and Elastic Inversion

et al. 2005

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“…There is a long history of trying to understand the relative importance of geological and engineering controls on reservoir performance (Kjønsvik et al, 1994; Manzocchi, Carter, et al, 2008; Manzocchi, John Matthews, et al, 2008; Weber & van Geuns, 1990). Some of these have focused on the geological (Manzocchi, Carter, et al, 2008; Manzocchi, John Matthews, et al, 2008; Skorstad et al, 2005) while others spotlight the engineering (Gurbanov et al, 2016; Matthews et al, 2008; Muggeridge et al, 2014) controls. A number of previous articles have attempted to look into both using statistical correlations, but they have traditionally struggled with the large numbers of parameters and the vast parameter space.…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal distribution of hydrocarbon production on the UK continental shelf

Osah,

Howell

2023

Resource Geology

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Hydrocarbon production from oil and gas fields is controlled by a variety of interconnected factors with a hierarchy of significance that is, for the most part, difficult to untangle. This article documents and investigates the spatial and temporal distribution of key hydrocarbon field parameters on the UK Continental Shelf. Data have been compiled from publicly available sources for 424 fields. Variables are considered as “descriptive parameters,” “control parameters” and “outcomes”. Descriptive parameters are metadata such as field name, location, etc. Control Parameters include depositional environment, present depth of burial, porosity, permeability, reservoir formation pressure, reservoir temperature, average net‐to‐gross, number of fault populations, hydrocarbon API, field area, bulk rock volume, well density, number of wells (production and injection), well spacing, gas oil ratio, reservoir thickness, fluid saturation, compartmentalization (quantitated by number of observable non‐communicating fault compartments), structural complexity (scaled from 0 to 5), field production strategy, trap type and stratigraphic heterogeneity. Outcomes are used to assess field performance and include final recovery factor (estimated), maximum production rate, and cumulative monthly production. Analysis of the database illustrates a number of empirical observations regarding hydrocarbon production on the UKCS. The Jurassic plays have been the most successful in the region in terms of total volumes produced while the Permian reservoirs of the SNS account for the majority of the gas. Most of the UKCS reservoirs record top depths between 2000 and 4500 m with good reservoir quality. The best reservoir quality is observed in reservoirs that were deposited within deep marine systems. The largest hydrocarbon reserves are found in the Northern North Sea basin in these deep marine (as well as paralic and shallow marine) reservoirs. Using the data from this article and affiliated data, potential exists for extracting insight beyond spatio‐temporal distributions.

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Using Multiple Grids in Markov Mesh Facies Modeling

et al. 2013

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A multigrid Markov mesh model for geological facies is formulated by defining a hierarchy of nested grids and defining a Markov mesh model for each of these grids. The facies probabilities in the Markov mesh models are formulated as generalized linear models that combine functions of the grid values in a sequential neighborhood. The parameters in the generalized linear model for each grid are estimated from the training image. During simulation, the coarse patterns are first laid out, and by simulating increasingly finer grids we are able to recreate patterns at different scales. The method is applied to several tests cases and results are compared to the training image and the results of a commercially available snesim algorithm. In each test case, simulation results are compared qualitatively by visual inspection, and quantitatively by using volume fractions, and an upscaled permeability tensor. When compared to the training image, the method produces results that only have a few percent deviation from the values of the training image. When compared with the snesim algorithm the results in general have the same quality. The largest computational cost in the multigrid Markov mesh is the estimation of model parameters from the training image. This is of comparable CPU time to that of creating one snesim realization. The simulation of one realization is typically ten times faster than the estimation.

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Sensitivity of Oil Production to Petrophysical Heterogeneities

Cited by 9 publications

References 7 publications

Information Content in Forward 4D-Seismic Modelling and Elastic Inversion

Information Content in Forward 4D-Seismic Modelling and Elastic Inversion

Spatial and temporal distribution of hydrocarbon production on the UK continental shelf

Using Multiple Grids in Markov Mesh Facies Modeling

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