Pressure Transient Formation and Well Testing
Convolution, Deconvolution and Nonlinear Estimation
- 1st Edition, Volume 57 - August 4, 2010
- Authors: Fikri J. Kuchuk, Mustafa Onur, Florian Hollaender
- Language: English
- Paperback ISBN:9 7 8 - 1 - 4 9 3 3 - 0 2 4 0 - 6
- Hardback ISBN:9 7 8 - 0 - 4 4 4 - 5 2 9 5 3 - 4
- eBook ISBN:9 7 8 - 0 - 0 8 - 0 9 3 1 7 4 - 6
This reference presents a comprehensive description of flow through porous media and solutions to pressure diffusion problems in homogenous, layered, and heterogeneous reservoirs.… Read more
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Request a sales quote- Serves as a training manual for geologists, petrophysicists, and reservoir engineers on formation and pressure transient testing
- Offers interpretation techniques for immediate application in the field
- Provides detailed coverage of pretests, multiprobe and packer pressure transient tests, including derivative, convolution, and pressure-rate and pressure-pressure deconvolution
1 Convolution
1.1 Introduction
1.2 Convolution Integral
1.3 Discrete Convolution
1.4 Duhamels (Superposition) Theorem and Pressure-Rate Convolution
1.5 Wellbore Pressure for Certain Variable Sandface Flow Rate Schedules
1.5.1 Polynomial Rate Functions
1.5.2 Exponential Flow Rate
1.6 Logarithmic Convolution (Multirate Well Testing)
1.7 Rate-Pressure Convolution
1.8 Pressure-Pressure Convolution
2 Deconvolution
2.1 Introduction
2.2 Analytical Methods
2.3 Direct Numerical Deconvolution
2.4 Deconvolution with Constraints
2.5 Nonlinear Least-Squares Pressure-Rate Deconvolution
2.5.1 Deconvolution, Gauge Resolution, and Radius of Investigation
2.5.2 Practicalities of Deconvolution Implementation
2.5.3 Deconvolution Parameters Selection
2.6 Pressure-Rate Deconvolution Examples
2.6.1 Simulated Well Test Example
2.6.2 Horizontal Field Test Example
2.6.3 Wirelin Formation Tester Example
2.7 Pressure-Pressure Deconvolution
2.8 Example Pressure-Pressure Deconvolution Examples
2.8.1 Logarithmic Convolution Example: Limited-Entry Well A
3 Nonlinear Parameter Estimation
3.1 Introduction
3.2 Overview
3.3 Parameter Estimation Methods
3.4 Likelihood Function and Maximum Likelihood Estimate
3.4.1 Single-Parameter Linear Model
3.4.2 Single-Parameter Nonlinear Model
3.5 Extension of Likelihood Function to Multiple Sets of Observed Data
3.6 Least-Squares Estimation Methods
3.7 Maximum Likelihood Estimation for Unknown Diagonal Covariance Case
3.7.1 MLE, WLS, and UWLS Estimates for a Single-Parameter Linear Model
3.7.2 A Synthetic Example Application Based on A Two-Parameter Linear Model with Three Sets of Observed Data
3.8 Use of Prior Information In MLE Estimation: The Bayesian Framework
3.8.1 Single-Parameter Linear Model Case
3.8.2 A Synthetic Example Application Based on A Two-Parameter Linear Model with Three Sets of Observed Data
3.9 Simultaneous vs. Sequential History Matching of Multiple IPTT Data Sets
3.10 Summary on MLE and LSE Methods
3.11 Minimization of MLE and LSE Objective Functions
3.12 Constraining Unknown Parameters In Minimization
3.13 Computation of Sensitivity Coefficients
3.14 Statistical Inference for MLE
3.15 IPTT Example Applications
3.15.1 Example 1
3.15.2 Example 2
3.15.3 Example 3
3.15.4 Example 4
3.15.5 Example 5
3.15.6 Example 6
- No. of pages: 416
- Language: English
- Edition: 1
- Volume: 57
- Published: August 4, 2010
- Imprint: Elsevier Science
- Paperback ISBN: 9781493302406
- Hardback ISBN: 9780444529534
- eBook ISBN: 9780080931746
FK
Fikri J. Kuchuk
MO
Mustafa Onur
FH