PVT Property Correlations

Selection and Estimation

Ahmed El-Banbi, Ahmed Alzahabi, Ahmed El-Maraghi

• Publisher: Gulf Professional Publishing
• Year: 2018
• Print ISBN: 9780128125724
• eBook ISBN: 9780128125731
• Topic: Technology & Engineering
• Subtopic: Chemical & Biochemical Engineering
• Index: Technology & Engineering

PVT properties are necessary for reservoir/well performance forecast and optimization. In absence of PVT laboratory measurements, finding the right correlation to estimate accurate PVT properties could be challenging. PVT Property Correlations: Selection and Estimation discusses techniques to properly calculate PVT properties from limited information. This book covers how to prepare PVT properties for dry gases, wet gases, gas condensates, volatile oils, black oils, and low gas-oil ration oils. It also explains the use of artificial neural network models in generating PVT properties. It presents numerous examples to explain step-by-step procedures in using techniques designed to deliver the most accurate PVT properties from correlations. Complimentary to this book is PVT correlation calculator software. Many of the techniques discussed in this book are available with the software. This book shows the importance of PVT data, provides practical tools to calculate PVT properties, and helps engineers select PVT correlations so they can model, optimize, and forecast their assets.- Understand how to prepare PVT data in absence of laboratory reports for all fluid types- Become equipped with a comprehensive list of PVT correlations and their applicability ranges- Learn about ANN models and their applications in providing PVT data- Become proficient in selecting best correlations and improving correlations results

Chapter 1

Introduction

Abstract

This chapter introduces the importance of using accurate pressure, volume, and temperature (PVT) properties required for most reservoir-, production-, and surface-processing calculations. Inaccurate estimation of PVT properties can lead to significant errors in calculation results. A comparison between saturated and undersaturated reservoirs using phase behavior is given. The chapter also discusses the common PVT models and compares their requirements and applications. It starts with black-oil models definition as fluid models that assume the composition of produced oil (stock-tank oil) and produced gas on surface to remain constant. It then explains both the similarity and differences of modified black-oil model to black-oil models. The differences between these models and the compositional model are also highlighted.

Keywords

Saturated and undersaturated reservoirs; black-oil models; modified black-oil (MBO) models; compositional models

The accuracy of many petroleum engineering calculations (e.g., material balance calculations, reserves estimation, well test analysis, advanced production data analysis, nodal analysis, surface network modeling, surface separation, and numerical reservoir simulations) largely depends on the accuracy of pressure, volume, and temperature (PVT) data. In an ideal situation, PVT data are determined from laboratory experiments performed on representative fluid samples collected from wellhead, surface separators, or wellbore. PVT reports give the results of PVT experiments usually conducted at reservoir temperature. Other routine measurements are usually taken as part of monitoring programs in oil- and gas-field operations. These include frequent measurement of API gravity of stock-tank oil, dead oil viscosity at atmospheric temperature or at different temperatures, specific gravity of separator gas, and composition of separator gas. In some cases, bubble point and dew point pressure can be estimated from production data. In addition, downhole density of reservoir fluids can be estimated from measurements of pressure versus depth (repeat formation tester measurements, RFT).

Common hydrocarbon PVT properties that appear in many calculations in reservoir and production engineering include saturation pressure (bubble point pressure for oils and dew point pressure for gases); solution gas–oil ratio; vaporized oil–gas ratio; formation volume factors for oil and gas; oil and gas density; oil and gas viscosity; single- and two-phase z-factor for gases; and oil and gas isothermal compressibility. The generation of representative values for these properties is the focus of this book.

Other PVT properties that are used in specific applications are reviewed in other texts (McCain, 1990; Whitson and Brule, 2000; Ahmed, 2016; Caroll, 2009; Mullins et al., 2007). These include water PVT properties, surface tension, minimum miscibility pressure, K values, hydrate formation pressure and temperature, asphaltene onset pressure and temperature, and wax appearance temperature.

Importance of Accurate PVT Properties

PVT properties are required for most reservoir, production, and surface-processing calculations. Inaccurate estimation of PVT properties can lead to significant errors in calculation results. Spivey and Pursell (1998) studied the effects of errors in PVT data on well test analysis interpretation results. They showed that errors in fluid compressibility affect the interpreted distance to boundaries. Distance to boundaries affects calculation of reserves from well tests (as in the case of reservoir limit tests). Errors in fluid viscosity affect estimates of permeability from well test analysis. Errors in fluid compressibility and formation volume factor have minor effects on estimated skin factor. In dual porosity reservoirs, errors in formation volume factor affect the interporosity flow coefficient (which determines how much fluid is transferred from the matrix to the fracture system with pressure drop between the two systems). Formation volume factor also affects fracture conductivity estimates for hydraulically fractured wells.

Ambastha and van Kruysdijk (1993) performed an error analysis study to quantify the effect of errors in the material balance equation for volumetric gas reservoirs. They concluded that errors in gas PVT data (z-factor and two-phase z-factor) in addition to errors in reservoir pressure can produce significant errors in the calculated original gas-in-place (OGIP) volumes. They used Monte Carlo simulation techniques to generate many cases for investigation of the upper and lower bounds for OGIP estimation errors expected from the errors in input data. The reported errors in OGIP reached 80% or more. The authors also investigated the effect of depletion level on the severity of errors. From the reported data, it was concluded that at any level of depletion, errors in gas PVT properties can lead to significant errors in OGIP estimation.

Baker et al. (2003) studied the effect of PVT data errors on material balance equation results for oil reservoirs. They studied the use of PVT correlations to derive PVT data for material balance analysis. They concluded that deriving PVT data from correlations without tuning the correlation to match the solution gas–oil ratio above the bubble point pressure could lead to significant errors in results of material balance analyses. They also studied the effect of introduction of systematic and random errors into the PVT data for material balance analysis. They concluded that the impact of PVT errors on material balance results could be significant in two cases: (1) if the decrease in reservoir pressure over the production history of the reservoir is small or (2) if the oil is highly volatile.

Hutchinson (1951) reported that many parameters including oil and water compressibility and formation volume factors affect the analysis results in material balance and pressure build-up test analysis calculations. The effect of isothermal oil compressibility in undersaturated oil reservoirs is significant; hence, the importance of using accurate estimates.

Trengove et al. (1991) reported the effects of changing PVT data on simulation results in the case of a gas condensate reservoir. They used an equation of state (EOS) program to match the labo...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Biographies
• Foreword
• Preface
• Acknowledgments
• Chapter 1. Introduction
• Chapter 2. Reservoir-Fluid Classification
• Chapter 3. Dry Gases
• Chapter 4. Wet Gases
• Chapter 5. Gas Condensates
• Chapter 6. Volatile Oils
• Chapter 7. Black Oils
• Chapter 8. Low Gas–Oil Ratio Oils
• Chapter 9. Selection of PVT Correlations
• Chapter 10. Artificial Neural Network Models for PVT Properties
• Appendix A. Oil Correlations Formulae
• Appendix B. Gas Correlations Formulae
• Appendix C. Oil Correlations Range of Applicability
• Appendix D. Gas Correlations Range of Applicability
• Appendix E. Artificial Neural Network (ANN) Models Range of Applicability
• Appendix F. Worksheets for Oil PVT Correlations Selection
• Index