Core Analysis

A Best Practice Guide

Colin McPhee, Jules Reed, Izaskun Zubizarreta

• Publisher: Elsevier
• Year: 2015
• ISBN:
  9780444636577
• Topic: Tecnología e ingeniería
• Subtopic: Ingeniería química y bioquímica

Core Analysis: A Best Practice Guide is a practical guide to the design of core analysis programs. Written to address the need for an updated set of recommended practices covering special core analysis and geomechanics tests, the book also provides unique insights into data quality control diagnosis and data utilization in reservoir models.

The book's best practices and procedures benefit petrophysicists, geoscientists, reservoir engineers, and production engineers, who will find useful information on core data in reservoir static and dynamic models. It provides a solid understanding of the core analysis procedures and methods used by commercial laboratories, the details of lab data reporting required to create quality control tests, and the diagnostic plots and protocols that can be used to identify suspect or erroneous data.

• Provides a practical overview of core analysis, from coring at the well site to laboratory data acquisition and interpretation
• Defines current best practice in core analysis preparation and test procedures, and the diagnostic tools used to quality control core data
• Provides essential information on design of core analysis programs and to judge the quality and reliability of core analysis data ultimately used in reservoir evaluation
• Of specific interest to those working in core analysis, porosity, relative permeability, and geomechanics

Chapter 1

Best Practice in Coring and Core Analysis

Colin McPhee LR Senergy Ltd., UK

Jules Reed LR Senergy Ltd., UK

Izaskun Zubizarreta LR Senergy Ltd., UK

Abstract

Core analysis provides the only direct and quantitative measurement of “intact” oil and gas reservoir properties. It should provide the foundation of formation evaluation for building static and dynamic reservoir models. However, it is estimated that approximately 70% of core analysis data are unfit for purpose as a consequence of ill-conceived test programme design, lack of planning, poor laboratory practice, inadequate reporting standards and unrepresentative test samples or test conditions.

This chapter explains why a best practice guide to core analysis data acquisition is needed, and outlines a core analysis management framework to ensure effective quality control of test data and minimise data uncertainties. The objectives are to provide reservoir engineers, geoscientists and petrophysicists with a solid grounding in the acquisition, evaluation and implementation of reliable and representative routine core analysis and special core analysis data, and the essential knowledge to be able to judge the quality and reliability of core analysis data that are used in reservoir evaluation and characterisation.

Keywords

Formation evaluation

Core analysis

Quality control

Best practice

1.1 Core Analysis Data: The Foundation of Formation Evaluation

The primary goal of geologists and petrophysicists is to estimate the volume of hydrocarbons initially in place in a reservoir. The primary goal of the reservoir engineer is to understand the physics of the reservoir-fluid system so that the ultimate recovery of hydrocarbons is maximised in the most economic matter. Both require a detailed knowledge of the reservoir geometry, structure and the interaction between the reservoir and the fluids, either in place, or which may be introduced into the reservoir. In reservoir modelling, a computer model of the reservoir is constructed by geologists, petrophysicists and geophysicists to provide a description of the reservoir which is principally used to determine the volumes of hydrocarbon in place. This is normally referred to as a static model. Reservoir simulation models are constructed by reservoir engineers to describe and map the hydrocarbon recovery processes under different production mechanisms. These dynamic models are principally used to determine reserves and recovery factors and to predict hydrocarbon production profiles for economic analysis.

Both static and dynamic reservoir models draw on a variety of disparate data sources including regional geology, seismic, sedimentological modelling, drilling data, wireline and logging/measurement while drilling data, fluid pressures and rock and fluid property data. The nature and quality of the model input data change throughout the lifetime of a field, so it is important to constantly review data quality to minimise uncertainties and to include data quality assessment in reservoir modelling. The quantity and quality of data used for both static and dynamic reservoir modelling must always be fit for purpose and match the field development objectives.

Core is normally the only part of the (relatively) undisturbed reservoir formation we can actually see, touch and feel at the surface. Consequently, core analysis data should be the “ground truth”, or the foundation upon which integrated formation evaluation and reservoir characterisation rest. All other data sources are essentially remote, so reliable and representative core analysis data are essential to calibrate and validate other data.

For example, the volume of stock tank oil initially in place (OIIP) in a reservoir can be determined from

$\mathrm{OIIP}=\mathrm{G}\mathrm{R}\mathrm{V}\left[\frac{N}{G}\right]\varphi \left(1-\mathrm{S}\mathrm{w}\right)\frac{1}{B_{\mathrm{o}}}$

(1.1)

Determination of the gross rock volume (GRV) and gross factor (G) in the net/gross ratio (N/G) is the primary responsibility of geophysicists and geologists. The reservoir engineer is responsible for oil formation volume factor (B_o) from pressure, volume temperature (PVT) experiments. The petrophysicist is responsible for net (N), porosity (ϕ) and water saturation (Sw) where data input relies principally on logs. Reservoir net thickness is normally defined by a permeability cut-off, and high-resolution permeability data are only possible from core. Porosity interpretation (e.g. from density logs) should be verified by, or calibrated against, stressed core porosities. Resistivity log interpretation requires core electrical property measurements to quantitatively determine water saturation in clean formations, and normalised cation exchange capacity is required to correct formation resistivity response for the presence of conductive clays. Water saturation can be determined directly by extracting water from core using Dean Stark or retort methods or indirectly, from primary drainage capillary pressure measurements.

The typical core analysis tests which are offered by commercial core analysis vendors and used as data input in petrophysical static models are summarised in Table 1.1. Historically these tests were carried out only at ambient conditions (low or no confining stress; ambient laboratory temperature), but most commercial laboratories can now provide these tests at more representative reservoir-appropriate stress, fluid and temperature conditions.

Table 1.1

Typical Core Analysis Data Input to Volumetric Calculations (Static Models)

Parameter	Data Source	Test Methods
Net	Permeability	Air permeability (ambient or reservoir stress) Klinkenberg permeability (ambient or reservoir stress) Brine (water) permeability (ambient or reservoir stress) Probe permeability (ambient conditions)
Porosity	Density porosity	Helium porosity (ambient or reservoir stress) Resaturation porosity (ambient or reservoir stress)
Water saturation	Electrical parameters	Formation resistivity factor (ambient or reservoir stress) Resistivity index (ambient or reservoir-appropriate conditions) Wet chemistry cation exchange capacity (CEC) for shaly sands (ambient conditions) Multiple-salinity tests (normalised CEC) at ambient or reservoir stress
	Primary drainage capillary pressure	Low-pressure mercury injection (ambient or reservoir stress) High-pressure mercury injection (ambient conditions) Gas–water or oil–water porous plate (ambient or reservoir-appropriate conditions) Gas–water or oil–water centrifuge (ambient conditions or limited reservoir stress)
	Direct measurement	Retort extraction (ambient conditions) Dean-Stark extraction (ambient conditions)

As Dake (1991) points out, “determination of the recovery factor is the most important single task of the reservoir engineer”. Recovery factors may be determined on purely technical criteria, but, more probably, on economic or environmental terms. For example, hydrocarbon recovery efficiency in a waterflood in an oil reservoir is largely governed by the mobility ratio:

$M=\frac{M_{\mathrm{r}\mathrm{w}}}{M_{\mathrm{r}\mathrm{o}}}=\frac{\mathrm{k}\mathrm{r}\mathrm{w}}{\mathrm{k}\mathrm{r}\mathrm{o}}\frac{{\mu }_{\mathrm{w}}}{{\mu }_{\mathrm{o}}}$

(1.2)

where M_rw and M_ro are the relative mobilities of water and oil, respectively. The parameters kro and kro are the relative permeabilities to oil and water, and μ_w and μ_o are water and oil viscosities, respectively. If ...