Understanding the dominant pore structures is a crucial tool in modelling carbonate reservoirs. Information on pore structure can be obtained through image logs or core data. In situations where there is limited available data such as core analysis, other data sources such as rock cuttings should be explored. Once the pore structure is understood, the appropriate model can be chosen. It has been found that a modified approach to traditional Nuclear Magnetic Resonance (NMR) techniques gives improved accuracy where conventional methods fail. This method results in a better understanding of reservoir behaviour, which should ultimately result in increased hydrocarbon recovery.For this paper, samples from a highly heterogeneous carbonate database of petrophysical properties are reviewed against established classification methods, such as the Lucia, Choquette and Pray, and newer methods such as the Lonoy classification. Mercury Injection Capillary Pressure (MICP) and NMR data are used to investigate and relate rock types and their dominant pore structures, to measured pore throat and body sizes. Common permeability models are reviewed for each classification and the suitability of each model is assessed and ranked using Pearson's coefficient and a power function. This case study demonstrates where traditional NMR porosity-permeability models are successful and where they are not, and proposes a modified approach to modelling the rock types where traditional methods fail. The practicality of identifying these rock types from logs is discussed and recommendations are made for evaluating heterogeneous carbonate formations. This approach can be applied to heterogeneous carbonate formations where NMR logging runs have been conducted and core samples have been obtained. INTRODUCTIONIt is generally easier to relate log data to petrophysical properties, such as permeability, in clastic systems, rather than in carbonate systems. Carbonate systems have proven more challenging as these relationships are not as easily defined from log data alone. Use of core-analysis techniques such as thin-section image analysis, NMR scans and mercury injection (MICP) analyses have helped to better understand the mechanisms that control permeability in carbonate rock types.Rock typing is another method used to better predict petrophysical properties. There are many published methods for classifying clastic and carbonate rock types. Some of the more popular carbonate classification systems used in the Petrophysics community are the Archie (Archie 1952) and the Lucia (Lucia 1983(Lucia , 1995(Lucia , and 1999 classifications. These are based primarily on the principle that the pore-size distribution within a rock controls permeability and saturation. Information on the different poresizes present within a carbonate interval is usually obtained from laboratory analysis of core-plugs, as well as interpretation of log data from image or magnetic resonance tools.Multiple rock typing methods are used to predict permeability. The classical method used is ...

A major drawback of well testing is the high cost and associated environmental impacts. If we can find a new methodology in defining the dynamic properties of the reservoir whilst achieving the principal objectives of a welltest, then the total operating expenditure and damage to the environment can be significantly reduced. Securities and Exchange Commission, Volume 74, January 2009, pg 2166, highlights that if a reliable technology can be used as an alternative, providing it has been field tested and has demonstrated consistency, then there is sufficient justification for it to be accepted. These proposed forward modelling techniques are towards achieving that goal of predicting welltest permeability. One of the key dynamic properties is permeability. Permeability can be estimated from core, logs and welltests, but each of these approaches interrogates a different volume and thus the permeability estimate relates to a different scale of the reservoir. Trying to relate these scales remains a challenge to the industry. We describe new research towards establishing a novel workflow for clastic reservoirs that can be used to determine an upscaled permeability from core/log data. In developing this strategy we address questions such as, can we achieve the objectives of a welltest with a wireline dataset and can we utilize the permeability derived from logs to forward model the welltest pressure transient? In achieving this goal a flow based upscaling technique can be used to derive a tensor permeability to account for heterogeneities and used to construct a simple geological model with an effective radius of investigation similar to that of a welltest. In this case the pressure transient is interpreted using analytical techniques and used to determine an upscaled permeability. The success of these approaches is determined by comparing the results to real welltests in an existing database.

Log derived permeability averages in homogeneous clastic reservoirs most often matches the reservoir scale permeability determined from well testing. However, when it comes to heterolithic, anisotropic reservoirs such as observed in successions interpreted as turbidites, there can be significant differences between reservoir scale estimates and traditional techniques such as arithmetic and geometric averaging. This mismatch is often overlooked, although it can be critical when it comes to history matching production data. The fundamental flaw of the log-only approach is that it assumes permeability to be isotropic, and therefore ignores the three-dimensional nature of permeability. In heterogeneous clastic reservoirs, the anisotropies originating from various small scale sedimentary structures may have an influence on fluid flow at larger scales, impacting the observed reservoir-scale permeability.This paper demonstrates how data acquired from wireline logs and cores from very heterogeneous successions like turbidites may be used to consistently predict the reservoir scale permeability. The workflow consists in processing and integrating the data into a lamina-scale near-wellbore model containing structural as well as petrophysical properties; a flowbased upscaling method is then applied to provide a geologically consistent input into a single-well model, from which a typical well test scenario will be simulated; the resulting pressure transient is then analyzed to determine a simulated permeability-thickness product.Presented in this paper is an application to two wells intersecting a turbidite reservoir. This shows that the permeability obtained from this workflow is closer to the well test permeability when compared to log derived estimates from traditional averaging techniques.This method can be applied to obtain valid reservoir-scale permeability values that can then be compared to the actual well test result. However, it can also be applied in cases where the value of information processes prove well testing to be uneconomical; then, a global reservoir permeability value can still be obtained using the workflow described.

We present a work flow for joint inversion of sonic flexural-wave dispersion data and array-induction resistivity data acquired in a vertical well. The work flow estimates a pixel-based radial distribution of water saturation and porosity extending several feet into the formation at each log depth. Radial changes in saturation and porosity are caused by mud-filtrate invasion and mechanical damage, respectively. The flexural-wave and array-induction data have similar multiple investigation depths extending several feet into the formation. Furthermore, flexural-wave data are sensitive to porosity but have weak sensitivity to saturation, whereas induction data are sensitive to both porosity and saturation. Thus, integration of these data in a joint inversion can help to characterize the formation beyond the altered zone and reduce uncertainty of the interpretation. The work flow is validated on synthetic data for several scenarios of near-wellbore alteration. The work flow is then applied to field data from an offshore well drilled with oil-based mud in a gas-bearing clastic formation. The results are compared with traditional interpretation and core analysis, demonstrating an efficient and accurate inversion-based work flow that can complement traditional formation evaluation in challenging conditions.

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