Investigation of the permeability of carbonate rocks is essential and challenging due to the heterogeneity of carbonates at all scales. At the micro-scale, pore geometry, pore size distribution, and pore connectivity are important factors controlling permeability. This study focuses on the influence of pore size distribution and pore structure on permeability to better understand the fluid flow in carbonate rocks.
In this paper, we use micro-computer tomography (micro-CT) to capture the microscopic heterogeneity in the pore structure. Firstly, we collected seven 1 x 6 inch carbonate rock samples including Indiana Limestone, Desert Rose, and Travertine with various porosities and permeabilities. The porosity was measured gravimetrically, and permeability was measured with core plug flooding experiments. Cubic centimeter size core samples were scanned with enhanced micro-CT imaging with the resolution of 6-8 μm/voxel, then scanned 2D images were processed with image processing software to distinguish the pore system from the matrix. The pore size distribution for each rock sample was determined by fitting a statistical function based on the binarized images. We defined a concept of equivalent pore radius to characterize the pore system, which effectively filters out the non-contributing small pores and preserves the pores actually contributing to fluid flow. The relationship between the equivalent pore radius of each rock and permeability was investigated. Based on the 2D image stack, we also constructed the 3D pore network to observe the pore structure, quantify connectivity and specific surface ratio to study their influence on permeability.
We found that laboratory measured permeability from core plugs was strongly correlated to the equivalent pore radius calculated from micro-CT scanned images among the investigated carbonate rock samples. The semilogarithmic correlation between permeability and effective pore radius fit the measured permeability data very well over a permeability range of more than two orders of magnitude. The findings of pore-scale pore structure and pore size distribution in this study are helpful for carbonate rock analysis, and the proposed new correlation between equivalent pore radius and permeability is practical for permeability estimation for a wide range of carbonate rocks.
Rojas, Pedro A. Romero (Weatherford International) | Cristea, Alexandrina (Weatherford International) | Pavlakos, Paul (Weatherford International) | Ergündüz, Okan (ARAR AS) | Kececioglu, Tayfun (ARAR AS) | Alpay, Server Fatih (ARAR AS)
Nuclear magnetic resonance wireline logging and data post-processing technologies are continuously evolving, making significant contributions to rock, fluid typing, formation evaluation and characterization of the near-wellbore zone. In heavy oil fields, however, nuclear magnetic resonance (NMR) logging is known to provide an underestimated permeability, poor reliable oil typing and thus poor oil saturation and viscosity determinations, especially when the evaluation is based only on the spectra of transverse magnetic relaxation times (T2) (one-dimension NMR) [Romero et al., 2009]. Several attempts have been made to improve NMR results, mostly with limited success [Fang et al., 2004], especially in separating the oil component from the contribution of other fluids to the T2 spectra. The main reason lies not necessarily in the selection of the data acquisition parameters and sequences for a single-frequency or multi-frequency tool, but in the way how the data is post-processed.
The present study refers to a well drilled through the Derdere formation, a limestone/dolomite heavy oil reservoir in Turkey. The NMR data was acquired in with a centralized, single-frequency wireline tool in a 6-in. borehole, drilled with water-based mud in a freshwater carbonate reservoir. The generated T2 log was analyzed in a traditional way to obtain the NMR total porosity and its partitions based on standard cutoff values. For the given 12 API oil gravity, reservoir temperature (76 °C) and gas-oil-ratio (GOR) the T2Oil peak appears around 170 ms, right from the T2 cutoff for limestones; therefore, no corrections were needed on the permeability calculated from the Timur-Coates and Schlumberger-Doll-Research (SDR) equations. In the present well, only a diffused separation between oil and free water could be observed on the T2 distribution log from field data.
In the broader concept of Artificial Intelligence, the newly proposed post-processing steps to obtain the oil saturation start by deconvolving the T2 spectra, using blind source separation (BSS) based on independent component analysis (ICA) [Romero, 2016; Romero Rojas et al., 2018]. Based on its T2 peak value —the expected T2Oil peak response— calculated from the prejob planner/simulator, the deconvolution results show that one specific independent component corresponds to the oil, from which the oil saturation was determined.
Results demonstrated the usefulness of NMR logging technology in the characterization and evaluation of this reservoir. Data post-processing based on BBS-ICA enable adequate differentiation between fluid components from T2 spectra. For the reasons above, NMR has been proposed for additional wells in the same field.
In the past, much of the petrophysics done in the Australian mining industry has been based upon gamma ray, simple density devices, resistivity, and televiewers. Common uses of petrophysical data include locating the top and bottom of the seam/ore, determining the water level, mapping fractures and faults, computing hardness, and facies analysis. However, the industry is moving toward more advanced applications, such as improved methods of understanding the porosity and permeability of the rocks, 3D mapping of stability, and the use of petrophysical measurements as a cost-effective means of supplementing or even replacing traditional assay methods.
This paper begins with a brief introduction to the mining environment as compared with the modern oilfield environment. While petrophysical data acquisition in East Australian coal mines is not so far removed from shallow oilfield land wells, open pit mines, such as the Pilbara Iron Ore fields of Western Australia are a very different world - thousands of holes are drilled, each generally less than 60 metres. Assays (geological analysis of material collected from the hole) are the primary reference data. Costs to log are low and many processes (data interpretation, delivery of logs, etc.) are automated.
Next we will review how gamma ray, density, neutron, resistivity, and caliper measurements are used throughout the Australian mining industry, paying some attention to the challenges of using classic tool designs such as 16/64 normal resistivity tools and single point (uncompensated) density. Sonic, electrical imaging, and optical televiewers are the next tier of measurements, used for fracture/fault mapping, ground stability, hardness and seismic integration. Finally, we will discuss the latest wave of technologies to be gaining ground in the Australian mining market, including NMR, VSP, and elemental spectroscopy.
The introduction of advanced petrophysical measurements in Australian mining is opening the door for exploiting new applications, many centered around “big data” or machine learning techniques, such as automated facies identification, high resolution mapping of both major and minor minerals, and 3D visualisation of ore properties.
The new-generation oil-base mud (OBM) microresistivity imagers provide photorealistic high-resolution quantified formation imaging. One of the existing interpretation methods is based on composite processing providing an apparent resistivity image largely free of the standoff effect. Another one is the inversion-based workflow, which is an alternative quantitative interpretation, providing a higher quality resistivity image, button standoff, and formation permittivities at two frequencies. In this work, a workflow based on artificial neural networks (NNs) is developed for quantitative interpretation of OBM imager data as an alternative to inversion-based workflow.
The machine learning approach aims to achieve at least the inversion-level quality in formation resistivity, permittivity, and standoff images an order of magnitude faster, making it suitable for implementation on automated interpretation services as well as integration with other machine learning based algorithms. The major challenge is the underdetermined problem since OBM imager provides only four measurements per button, and eight model parameters related to formation, mud properties, and standoff need to be predicted. The corresponding nonlinear regression problem was extensively studied to determine tool sensitivities and the combination of inputs required to predict each unknown parameter most accurately and robustly. This study led to the design of cascaded feed-forward neural networks, where one or more model parameters are predicted at each stage and then passed on to following steps in the workflow as inputs until all unknowns are accurately obtained.
Both inverted field data sets and synthetic data from finite-element electromagnetic modeling were used in multiple training scenarios. In the first strategy, field data from few buttons and existing inversion results were used to train a single NN to reproduce standoff and resistivity images for all other buttons. Although the generated images are comparable to images coming from inversion, the method is dependent on the availability of field data for variable mud properties, which at the moment limits the generalization of the NNs to diverse mud and formation properties.
In the second strategy, we utilized the synthetic responses from a finite element model (FEM) simulator for a wide range of standoffs, formation, and mud properties to develop a cascaded workflow, where each stage predicts one or more model parameters. Early stages of the workflow predict the mud properties from low formation resistivity data sections. NNs then feed the estimated mud angle and permittivities at two frequencies into next stages of the workflow to finally predict standoff, formation resistivity, and formation permittivities. Knowledge of measurement sensitivities was critical to design the efficient parameterization and robust cascaded neural networks not only due mathematically underdetermined nature of the problem but also the wide dynamic range of mud and formation properties variation and the measurements. Results for processed resistivity, standoff, and permittivity images are presented, demonstrating very good agreement and consistency with inversion-generated images. The combination of two strategies, training on both synthetic and field data, can lead to further improvement of robustness allowing customization of interpretation applications for specific formations, muds, or applications.
Copyright 2019 held jointly by the Society of Petrophysicists and Well Log Analysts (SPWLA) and the submitting authors. ABSTRACT Today, many machine learning techniques are regularly employed in petrophysical modelling such as cluster analysis, neural networks, fuzzy logic, self-organising maps, genetic algorithm, principal component analysis etc. While each of these methods has its strengths and weaknesses, one of the challenges to most of the existing techniques is how to best handle the variety of dynamic ranges present in petrophysical input data. Mixing input data with logarithmic variation (such as resistivity) and linear variation (such as gamma ray) while effectively balancing the weight of each variable can be particularly difficult to manage. DTA is conceived based on extensive research conducted in the field of CFD (Computational Fluid Dynamics). This paper is focused on the application of DTA to petrophysics and its fundamental distinction from various other statistical methods adopted in the industry. Case studies are shown, predicting porosity and permeability for a variety of scenarios using the DTA method and other techniques. The results from the various methods are compared, and the robustness of DTA is illustrated. The example datasets are drawn from public databases within the Norwegian and Dutch sectors of the North Sea, and Western Australia, some of which have a rich set of input data including logs, core, and reservoir characterisation from which to build a model, while others have relatively sparse data available allowing for an analysis of the effectiveness of the method when both rich and poor training data are available. The paper concludes with recommendations on the best way to use DTA in real-time to predict porosity and permeability. INTRODUCTION The seismic shift in the data analytics landscape after the Macondo disaster has produced intensive focus on the accuracy and precision of prediction of pore pressure and petrophysical parameters.
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Unmanned minimum facility platforms are a reliable alternative to traditional wellhead platforms or subsea installations, and the technologies enabling simpler designs have evolved. The technologies born out of innovative ideas have been critical for advancing deepwater assets in the past, and venture-capital investment helps incubate risk-taking companies developing those technologies. With digitization becoming a greater focus in industry, what role will venture capital play?
The decision may alleviate some of the pressures oil and gas producers faced in the wake of their imposition last year. Canada and Mexico made up a combined 20% of US imports of oil country tubular goods in 2017. An EIA report shows natural gas exports reaching 4.6 Bcf/D in February, the 13th consecutive month in which the country's natural gas exports exceeded its imports. Exports are projected to reach 7.5 Bcf/D by 2020.
The initial high cost of exploitation of the sustained, increasingly growing development of unconventional resources in Argentina has resulted in concentrating all efforts to increase well productivity while reducing construction and completion costs. The optimization of hydraulic fracture (HF) treatments is vitally important. It is the primary strategy used to achieve an optimal reservoir drainage area, consequently characterizing the fracture geometry, including the height, for the continuous improvement of HF treatment and planning.
Several types of technologies and methodologies are used to estimate fracture height during and after a hydraulic stimulation treatment. These technologies can provide information about the fracture geometry and extension in the near-wellbore (NWB) and far-field areas. The determination of a reliable correlation between those methodologies represents a challenge as a result of formation complexity, heterogeneity, and limitations of evaluation technologies. It is well-known that some areas in the Vaca Muerta formation contain layers that can act as fracture barriers and are responsible for fracture containment.
This paper presents a fast and simple methodology that uses conventional well logs [gamma ray (GR), sonic, and density] from pilot wells to identify potential fracture barriers. This approach establishes a means to evaluate the degree to which the rock will have the ability to control fracture height growth. This methodology was determined useful for planning perforation intervals or clusters placement, particularly in those formations with stress profile showing reduced stress contrast and, when complemented with geological information, this method also provides useful information for horizontal well trajectory. Case studies are provided to illustrate examples of the proposed fracture barrier index (FBI) being calibrated or compared to other fracture height assessment. Additionally, the benefits of adding this new approach to current methodologies and technologies to aid completion design optimization and decision making is discussed.