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Experimental design in petroleum reservoir studies

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Experimental design in petroleum reservoir studies/ Mohammad Jamshidnezhad.
Author:	Jamshidnezhad, Mohammad,
Published:	Oxford, UK :Elsevier : : 2015.,
Description:	1 online resource.
Subject:	Oil reservoir engineering - Mathematical models. -
Online resource:	https://www.sciencedirect.com/science/book/9780128030707
ISBN:	9780128030714 (electronic bk.)

Experimental design in petroleum reservoir studies
Jamshidnezhad, Mohammad,

Experimental design in petroleum reservoir studies[electronic resource] /Mohammad Jamshidnezhad. - Oxford, UK :Elsevier :2015. - 1 online resource.

Includes bibliographical references and index.

Front Cover; Experimental Design in Petroleum Reservoir Studies; Copyright Page; Contents; Biography; Preface; 1 Introduction; 1.1 Petroleum reservoirs; 1.2 Petroleum rock properties; 1.3 Volumetric calculations in a reservoir; 1.4 Reservoir heterogeneity; 1.5 Reservoir models; 1.6 Experimental design; 2 Reservoir modeling; 2.1 Introduction; 2.2 Sources of data for reservoir modeling; 2.3 Reservoir characterization; 2.3.1 Geophysical and geological data; 2.3.2 Engineering data; 2.3.2.1 Core data; 2.3.2.2 Well logging data; 2.3.2.3 Pressure transient data.

One of the main duties for reservoir engineers is reservoir study, which starts when a reservoir is explored and it continues until the reservoir abandonment. Reservoir study is a continual process and due to various reasons such as complexity at the surface and limited data, there are many uncertainties in reservoir modelling and characterization causing difficulties in reasonable history-matching and prediction phases of study. This book concentrates on experimental design, a trusted method in reservoir management, to analyze and take the guesswork out of the uncertainties surrounding the underdeveloped reservoir. Case studies from the Barnett shale and fractured reservoirs in the Middle East are some of the examples included. Other discussions on uncertainty in PVT, field performance data, and relevant outcomes of experimental design will provide insight into how better data can improve measurement tools, your model, and your reservoir assets. --

English.

ISBN: 9780128030714 (electronic bk.)Subjects--Topical Terms:

527872
Oil reservoir engineering
--Mathematical models.Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: TN870.57

Dewey Class. No.: 622/.3382

Experimental design in petroleum reservoir studies
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100 1 $a Jamshidnezhad, Mohammad, $e author. $3 1292835
245 1 0 $a Experimental design in petroleum reservoir studies $h [electronic resource] / $c Mohammad Jamshidnezhad.
260 $a Oxford, UK : $b Elsevier : $b Gulf Professional Publishing, $c 2015.
300 $a 1 online resource.
504 $a Includes bibliographical references and index.
505 0 $a Front Cover; Experimental Design in Petroleum Reservoir Studies; Copyright Page; Contents; Biography; Preface; 1 Introduction; 1.1 Petroleum reservoirs; 1.2 Petroleum rock properties; 1.3 Volumetric calculations in a reservoir; 1.4 Reservoir heterogeneity; 1.5 Reservoir models; 1.6 Experimental design; 2 Reservoir modeling; 2.1 Introduction; 2.2 Sources of data for reservoir modeling; 2.3 Reservoir characterization; 2.3.1 Geophysical and geological data; 2.3.2 Engineering data; 2.3.2.1 Core data; 2.3.2.2 Well logging data; 2.3.2.3 Pressure transient data.
505 8 $a 2.3.2.4 Properties of reservoir fluids2.3.2.5 Rock-Fluid data; 2.3.2.6 Initialization data; 2.3.2.7 Well and recurrent data; 2.4 Mathematical modeling; 2.4.1 Decline curve analysis; 2.4.2 Analytical models; 2.4.3 Numerical simulation; 2.5 Model verification; 3 Experimental design in reservoir engineering; 3.1 Introduction; 3.2 Errors in mathematical modeling; 3.3 Uncertainty in reservoir data; 3.3.1 Uncertainty in geophysical data; 3.3.2 Uncertainty in geological data; 3.3.3 Uncertainty in dynamic data; 3.3.4 Uncertainty in PVT data; 3.3.5 Uncertainty in field performance data.
505 8 $a 3.4 Uncertainty analysis3.4.1 History matching; 3.4.2 Stochastic methods for uncertainty analysis; 3.4.2.1 Basic definitions; 3.4.3 Monte Carlo simulation; 3.5 Experimental design; 3.5.1 Basic rules in experimental design; 3.5.2 Outcomes of experimental design; 3.5.3 Designs; 3.5.3.1 Two-level full factorial designs; 3.5.3.2 Two-level fractional factorial designs; 3.5.3.3 Plackett-Burman design; 3.5.3.4 Three-level designs; 3.5.3.5 Latin hypercube design; 3.5.4 Response surface; 3.5.5 Sensitivity analysis; 3.5.5.1 Sensitivity analysis work flow; 4 Case studies; 4.1 Introduction.
505 8 $a 4.2 Case study 14.2.1 Ninth SPE comparative solution problem; 4.2.2 Uncertain parameters; 4.2.3 Experimental design; 4.2.4 Response surfaces; 4.3 Case study 2; 4.3.1 Undersaturated fractured reservoir in the middle east; 4.3.2 Uncertainty parameters; 4.4 Case study 3; 4.4.1 PUNQ case; 4.4.2 Uncertain parameters; 4.5 Case study 4; 4.5.1 Steam assisted gravity drainage in a heavy oil reservoir; 4.5.2 Experimental design; 4.6 Case study 5; 4.6.1 Barnett shale gas reservoir; 4.6.2 Reservoir modeling; 4.6.3 Uncertainty parameters; 4.6.4 Experimental design; 4.7 Case study 6.
505 8 $a 4.7.1 Miscible WAG injection4.7.2 Reservoir modeling; 4.7.3 Uncertain parameters; 4.7.4 Experimental design; Appendix: F distribution values; References; Index.
520 $a One of the main duties for reservoir engineers is reservoir study, which starts when a reservoir is explored and it continues until the reservoir abandonment. Reservoir study is a continual process and due to various reasons such as complexity at the surface and limited data, there are many uncertainties in reservoir modelling and characterization causing difficulties in reasonable history-matching and prediction phases of study. This book concentrates on experimental design, a trusted method in reservoir management, to analyze and take the guesswork out of the uncertainties surrounding the underdeveloped reservoir. Case studies from the Barnett shale and fractured reservoirs in the Middle East are some of the examples included. Other discussions on uncertainty in PVT, field performance data, and relevant outcomes of experimental design will provide insight into how better data can improve measurement tools, your model, and your reservoir assets. -- $c Edited summary from book.
546 $a English.
588 0 $a Online resource; title from PDF title page (EBSCO, viewed April 22, 2015).
650 0 $a Oil reservoir engineering $x Mathematical models. $3 527872
650 0 $a Petroleum $x Geology $x Mathematical models. $3 786396
650 0 $a Petroleum engineering $x Mathematics. $3 1292836
655 4 $a Electronic books. $2 local $3 554714
856 4 0 $u https://www.sciencedirect.com/science/book/9780128030707