Modelling the Effects of Faults and Fractures on Fluid Flow in Petroleum Reservoirs

Harris, S.D.; Vaszi, A.Z.; Fisher, Q.J.; Karimi-Fard, Mohammad; Wu, Kaihua

doi:10.1016/b978-008044490-1/50021-1

Cited by 11 publications

(10 citation statements)

References 46 publications

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“…In the few past decades, there are considerable and reawakening interests in the EOF in porous media because of the conspicuous applications in biological-chemical-medical analysis [8][9][10][11] and new techniques in energy and geophysical engineering [12][13][14][15], especially in micro-and nano-scales [16][17][18]. Recently, charged porous structures have been employed in some devices to control and improve the fluid behavior as expected.…”

Section: Introductionmentioning

confidence: 99%

Electroosmosis in homogeneously charged micro- and nanoscale random porous media

Wang

Chen

2007

Journal of Colloid and Interface Science

121

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Electroosmosis in homogeneously charged micro-and nanoscale random porous media has been numerically investigated using mesoscopic simulation methods which involve a random generation-growth method for reproducing three-dimensional random microstructures of porous media and a high-efficiency lattice Poisson-Boltzmann algorithm for solving the strongly nonlinear governing equations of electroosmosis in three-dimensional porous media. The numerical modeling and predictions of EOF in micro-and nanoscale random porous media indicate: the electroosmotic permeability increases monotonically with the porosity of porous media and the increasing rate rises with the porosity as well; the electroosmotic permeability increases with the average solid particle size for a given porosity and with the bulk ionic concentration as well; the proportionally linear relationship between the electroosmotic permeability and the zeta potential on solid surfaces breaks down for high zeta potentials. The present predictions agree well with the available experimental data while some results deviate from the predictions based on the macroscopic theories.

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Section: Introductionmentioning

confidence: 99%

Electroosmosis in homogeneously charged micro- and nanoscale random porous media

Wang

Chen

2007

Journal of Colloid and Interface Science

121

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“…Møller-Pedersen & Koestler 1997;Coward et al 1998;Jones et al 1998;Nieuwland 2003;McClay 2004;Swennen et al 2004;Boult & Kaldi 2005;Shaw 2005;Sorkhabi & Tsuji 2005;, Jolley et al 2007aLonergan et al 2007;Wibberley et al 2008a). In understanding the uncertainties and risks from compartmentalization these largely lie in the areas of: reservoir scale structural analysis Dee et al 2005;Fossen & Bale 2007); fault and fracture growth, fault zone architecture and characterization Wibberley et al 2008b); fault seal prediction (Bretan et al 2003;Dee et al 2007); the incorporation of faults and fault zone properties in reservoir models and simulation (Manzocchi et al 1999(Manzocchi et al , 2002(Manzocchi et al , 2008Harris et al 2005Harris et al , 2007Childs et al 2007;Fisher & Jolley 2007;Zijlstra et al 2007); integrated fluid description (Smalley & England 1994;Larter & Aplin 1995;Smalley et al 2004); the use of geomechanical data along with field and inter-well scale stress and strain analysis and modelling -incorporating the use of micro-seismicity for surveillance (Heffer 2002;Rutledge et al 2004;Sanderson & Zhang 2004;Main et al 2007;Zhang et al 2007;Osorio et al 2008). …”

Section: The Integrated Reservoir and Fluids Description Toolkitmentioning

confidence: 99%

The challenges and impact of compartmentalization in reservoir appraisal and development

Fox

Bowman

2010

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As an industry we have been poor at identifying and predicting the effect of reservoir compartmentalization on fluid flow throughout field life. In the context of harder-to-find reserves and rising development costs it is vital to have a well-rounded strategy in place to identify and mitigate uncertainties and risks associated with compartmentalization. The key challenge of today is therefore to improve predictive capability.Historically we have relied too heavily on 'single complex' linear modelling approaches to understand the impact of compartmentalization. Lately, we have begun to place a greater emphasis on using a forensic level of reservoir analysis coupled with the use of dynamic signals from production data and constant down hole monitoring of fluid-type, pressures and temperatures. Evidence is mounting that as fields deplete they evolve mechanically over production time-scales leading to changes in fault behaviour, stress configuration, compaction and hence compartmentalization; such factors are commonly not predicted at start-up. Our challenge has been to develop toolkits and workflows which integrate an appropriate range of geological models iteratively coupled with dynamic data. We need to develop analytical approaches that enable real-time updates from the evolving reservoir & fluid system to iteratively modify our models and improve their predictive power. This will allow us to make better-informed decisions at every stage of field life.The upstream exploration and production business is commonly driven by the expectation that we can now get more from our older existing fields, continue with positive reserve replacement ratios (the ratio between: reserves booked from discoveries, field extensions and improved recovery schemes; and production over a given period), manage rising costs, and make better informed decisions when faced with increased geological complexity in new developments. Thus, production teams are pressured to deliver on promises derived from geological models and production rate profiles experienced and/or predicted earlier in a field's Appraisal or Development phase. There are, however, many examples in the literature where compartmentalization has unexpectedly impacted field development (e.g. Gainski et al. 2010;McKie et al. 2010). It follows that successful production optimization is controlled by our ability to characterize and predict reservoir performance and fluid flow behaviour over the life-cycle of a hydrocarbon field. In many cases, this understanding comes through experience, relatively late in field life (e.g. Barkved et al. 2003;Clifford et al. 2005;Moulds et al. 2005;Barr et al. 2007;Gainski et al. 2010). Understanding the impact of compartmentalization on fluid flow and the ability to predict it prior to drilling wells is a long-term intention of many companies, but it is consistently under-evaluated and underestimated. It would appear that we still have some way to go before we consistently predict the impact of faults, fractures, stress and other reservoir heteroge...

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“…Analysis of the impact of damage zones surrounding seismically imaged faults reveals similar features, with newly developed methods being capable of exploring the sensitivity of flow to the full range of geometric and scaling parameters associated with damage zones (see Harris et al and references therein). Harris et al (1999Harris et al ( , 2005, this volume) extend these sensitivity studies into 3D, and make a strong case for the routine definition of damage zone effective properties and their implicit inclusion in reservoir flow simulations. Ma et al show the extent to which modelling of small fault arrays is sensitive to the modelling methods used, including such issues as discretization and the preservation of fault connectivity.…”

Section: Upscaling the Flow Effects Of Subseismic Faultsmentioning

confidence: 99%

Structurally complex reservoirs: an introduction

Jolley

Barr

Walsh

et al. 2007

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Structurally complex reservoirs form a distinct class of reservoir, in which fault arrays and fracture networks, in particular, exert an over-riding control on petroleum trapping and production behaviour. With modern exploration and production portfolios commonly held in geologically complex settings, there is an increasing technical challenge to find new prospects and to extract remaining hydrocarbons from these more structurally complex reservoirs. Improved analytical and modelling techniques will enhance our ability to locate connected hydrocarbon volumes and unswept sections of reservoir, and thus help optimize field development, production rates and ultimate recovery. This volume reviews our current understanding and ability to model the complex distribution and behaviour of fault and fracture networks, highlighting their fluid compartmentalizing effects and storage-transmissivity characteristics, and outlining approaches for predicting the dynamic fluid flow and geomechanical behaviour of structurally complex reservoirs. This introductory paper provides an overview of the research status on structurally complex reservoirs and aims to create a context for the collection of papers presented in this volume and, in doing so, an entry point for the reader into the subject. We have focused on the recent progress and outstanding issues in the areas of: (i) structural complexity and fault geometry; (ii) the detection and prediction of faults and fractures; (iii) the compartmentalizing effects of fault systems and complex siliciclastic reservoirs; and (iv) the critical controls that affect fractured reservoirs.Structurally complex reservoirs form a distinct class of reservoir in which fault arrays and fracture networks, in particular, exert an over-riding control on petroleum trapping and production behaviour

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Modelling the Effects of Faults and Fractures on Fluid Flow in Petroleum Reservoirs

Cited by 11 publications

References 46 publications

Electroosmosis in homogeneously charged micro- and nanoscale random porous media

Electroosmosis in homogeneously charged micro- and nanoscale random porous media

The challenges and impact of compartmentalization in reservoir appraisal and development

Structurally complex reservoirs: an introduction

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