Asbjorn Gyllensten

Bio: Asbjorn Gyllensten is an academic researcher from Abu Dhabi Company for Onshore Oil Operations. The author has contributed to research in topics: Geostatistics & Saturation (chemistry). The author has an hindex of 5, co-authored 6 publications receiving 65 citations.

Quantitative Analysis of Porosity Heterogeneity: Application of Geostatistics to Borehole Images

Abstract: High levels of heterogeneity in many carbonate reservoirs have raised concerns about the validity and relevance of small-scale measurements from core plugs and high-resolution logs. While the measurements themselves may be accurate, they may not be representative of the average formation properties. A related question is one of reconciling the measurements made in small volume of investigation data (e.g., core plugs), with the measurements from relatively large volume of investigation data (e.g., wireline logs). This paper presents a technique to quantitatively describe the porosity heterogeneity in a borehole at the scale of several tenths of an inch. The method involves treating high-resolution borehole imagery as a 2D sample from a 3D data volume, and applying geostatistical analysis to these data. We compute the experimental semi-variogram and upscale its range and sill to larger (several inches) scales of measurement to predict the impact of heterogeneity on conventional core plug and logging tool porosity measurements. The resulting dispersion variance between the different measurement scales support the interpretation, application and comparison of these porosity measurements. This technique was applied to an Early Cretaceous carbonate reservoir in Abu Dhabi. We found that the scale of the heterogeneity is typically less than 1– 2in., so that while significant heterogeneity is observed at the core plug and smaller scales of measurement, the larger-volume logging tool measurements smooth out the heterogeneity and show considerably less variability. The differences between porosity measured in core plugs can be completely accounted for by this upscaling effect.

Carbonate rocks and petroleum reservoirs: a geological perspective from the industry

Abstract: Carbonate oil reservoirs are sometimes regarded with apprehension in the petroleum industry since it can be difficult to predict the quality of, and ensure high recovery factors from, this rock family. Particular problems are the complex and heterogeneous nature of porosity in carbonate rocks, often leading to large ranges in permeability for any given porosity, and the organization of carbonate successions most commonly as vertically heterogeneous, but laterally persistent, layers. Important issues that arise time and again in carbonate reservoir description include (a) predicting reservoir quality at inter-well scales and in uncored wells, (b) recognizing problematic high-permeability layers, (c) determining the permeability component to allocate to fractures and connected vug systems, and (d) populating reservoir models with representative physical parameters. Because porosity in carbonate rocks generally presents as diverse and heterogeneous, conventional core plugs are seldom representative of large rock volumes and significant issues remain in terms of the scale-compatibility of the various datasets for measured physical parameters that are used in carbonate reservoir description. Many of the world9s largest carbonate reservoirs were discovered and developed shortly after the Second World War and are now showing signs of maturity, expressed variously as poor pressure support, water or gas breakthrough and stranded resources. The proportion of the world9s ‘conventional’ petroleum that is reservoired in carbonate rocks is commonly estimated at around 50–60% and many large carbonate reservoirs are likely to have a production lifetime beyond 50 years. It is no coincidence then that the petroleum industry has been the primary source of funding of and promotion of research into carbonate rocks and depositional systems, often with impacts extending well beyond oil and gas exploitation.

Method to generate numerical pseudocores using borehole images, digital rock samples, and multi-point statistics

Abstract: Methods and systems for creating a numerical pseudocore model, comprising: a) obtaining logging data from a reservoir having depth-defined intervals of the reservoir, and processing the logging data into interpretable borehole image data having unidentified borehole image data; b) examining one of the interpretable borehole image data, other processed logging data or both to generate the unidentified borehole image data, processing the generated unidentified borehole image data into the interpretable borehole image data to generate warped fullbore image data; c) collecting one of a core from the reservoir, the logging data or both and generating a digital core data from one of the collected core, the logging data or both such that generated digital core data represents features of one or more depth-defined interval of the reservoir; and d) processing generated digital core data, interpretable borehole image data or the logging data to generate realizations of the numerical pseudocore model.

Modeling and Inversion Methods for the Interpretation of Resistivity Logging Tool Response

Abstract: The electrical resistivity measured by well logging tools is one of the most important rock parameters for indicating the amount of hydrocarbons present in a reservoir. The main interpretation challenge is to invert the measured data, solving for the true resistivity values in each zone of a reservoir. Inversion is not always an easy task because logging tools measure a bulk average resistivity. Thus reservoir heterogeneity can have a considerable effect on inversion accuracy. Two of the most significant problems are effects caused by regions adjacent to zones of interest and resistivity anisotropy (variation of resistivity with direction). The growing use of directional drilling has recently focused attention on the magnitude of anisotropy effect. Therefore this thesis concentrates on the new area of inversion in anisotropic reservoirs. The geologic origins of anisotropy are examined, and a parametric inversion method is introduced for obtaining directional resistivity values in layered media. Background is also provided on practical modeling methods for use in inversion, and on the physics of various resistivity loggingtools.

Lithofacies Clustering Using Principal Component Analysis and Neural Network: Applications to Wireline Logs

Abstract: Both statistical methods and artificial neural network (ANN) have been used for lithology or facies clustering. ANN, in particular, has increasingly gained popularity for clustering of categorical variables as well as for predictions of continuous variables. In this article, we discuss several counter examples that show deficiencies of these techniques when used for automatic lithofacies clustering. Our examples show that the lithofacies clustered by ANN alone or ANN in combination with principal component analysis (PCA), as commonly used, are highly inconsistent with the benchmark charts based on laboratory results. We propose several techniques to overcome these problems and improve the clustering of lithofacies, including (1) classification of lithofacies using the minor or intermediate principal component(s), (2) rotation of a principal component before using ANN for clustering, (3) cascading two or more PCAs and ANNs for clustering lithofacies or electrofacies, and (4) classifying lithofacies with demarcated stratigraphic reference classes.

Method to quantify discrete pore shapes, volumes, and surface areas using confocal profilometry

Abstract: Methods for characterizing a sample of porous media using a measuring device along with a multipoint statistical (MPS) model. Retrieving a set of reflected measured data provided by the measuring device of a surface of the sample in order to produce a sample imaging log, wherein the retrieved set of measured data is communicated to a processor. Selecting depth-defined surface portions of the sample from the sample imaging log as a training image for inputting in the MPS model. Determining pattern based simulations from the training image using one of a pixel-based template which is applied to the training image. Constructing from the pattern based simulations a complete-sampling image log of surface portions of the sample. Repeat the above steps in order to construct three dimensional (3D) sample images from stacked successive pattern based simulations so as to construct at least one 3D model of the sample.