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.
Selecting the best tool for a specific type of reservoir condition is a crucial part of a fluid sampling job. Moreover, uncertainty in sample quality increases when the fluid phases are miscible. On a recent logging job, a formation tester was used to acquire water samples across a zone drilled with water-base mud (WBM). We examine the performance of several probe configurations (both existing and prototype) under equivalent reservoir conditions to quantify and optimize filtrate cleanup efficiency. The study is carried out using a compositional simulator for a water-saturated reservoir invaded with blue-dye tracer included in WBM filtrate.
History matching of field measurements allows the calibration of the model for further modification to account for a variety of reservoir conditions. Complex tracer dynamics of a blue-dye WBM invading a water-saturated formation and fluid pumpout are accurately and expediently modeled using a flexible numerical algorithm to account for different probe types and tool configurations. Under normal operating conditions, the chosen formation tester would have taken around one hour to clean the filtrate contamination to a target value of 5%. On the other hand, the best choice was the Focused Elliptical Probe, for which fluid cleanup would take less than 40 minutes. Additionally, a different tool configuration with a combination of multiple probe geometries spaced radially around the tool would provide faster cleanup times of only 32 minutes, thereby saving rig time.
We rank eight formation testing tools designs under equivalent reservoir conditions. The examples highlight the importance of probe geometry and configurations together with reliable and expedient numerical modeling during the pre-job phase to reduce cleanup time in anticipation of complex reservoir conditions. Furthermore, numerical simulations compare the fluid cleanup efficiency for various commercial formation-testing probes together with innovative probe designs that could potentially lead to a new tool or probe development. Perfecting both probe geometry and fluid pumping schedule is the most important output of our study.
Pineda, Wilson (BP) | Wadsworth, Jennifer (BP) | Halverson, Dann (BP) | Mathers, Genevive (BP) | Cedillo, Gerardo (BP) | Maeso, Carlos (Schlumberger) | Maggs, David (Schlumberger) | Watcharophat, Hathairat (Schlumberger) | Xu, Weixin (Wayne) (Schlumberger)
Deepwater depositional environments in the Gulf of Mexico can be very complex. Accurate determination of depositional facies is important in these capital-intensive fields. The most common reservoir facies are laterally extensive sheet sandstones with thin mudrock layers, channel complexes (isolated or amalgamated) and channel-levee complexes (often with poor reservoir communication). Reservoirs are often complicated by steep dips close to salt domes and the presence of potential fluid conduits due to faults or fractures. Borehole images aid in determining the character of the sediments, as well as improve net sand calculations, and illuminate the geology in the near wellbore region both in structure and depositional environment, and to provide valuable geomechanics information for the determination of the stress vector.
A well was recently drilled through one of these deep water sediment sequences in the Gulf of Mexico with an oil-based mud (OBM) system. An extensive acquisition program included a series of logging while drilling (LWD) and wireline images. In addition to the current LWD lower resolution borehole imaging tools, a new LWD dual physics OBM imager was deployed for the first time in this field. Five different types of physics were acquired, including lower-resolution images from nuclear measurements (gamma ray, density and photoelectric) and the high-resolution images from dualphysics OBM imager (DPOI) which is based on resistivity and ultrasonic measurements. Wireline high-resolution OBM resistivity images were also acquired. This paper shows a comparison of images collected with the new DPOI versus traditional LWD images and high-resolution wireline resistivity images.
Comparisons of the types of features observed from the various imaging tools were made, showing how the differences in physics, resolution and time of logging affects the images, as well as the impact these factors can have on subsequent interpretations. Four main categories of features are included in comparisons between the tools: sand-rich sections, consistently dipping mudrocks, chaotic zones and fractures/faults. The different images allow fuller interpretation of the gross sequence. In general, the higher the resolution, the more detailed and confident the interpretation is, particularly where the hole conditions are good. In degraded borehole sections, the LWD acquisition was beneficial for obtaining images as early as possible, when damage was at a minimum. The impact of the differences in the physics depends on the properties and contrasts being imaged. This is observed with fractures - both conductive and resistive examples can be seen on both LWD and wireline images. The ultrasonic images are complementary with both low and high amplitude fractures seen, providing more confidence in the fracture interpretation.
Rabinovich, Michael (BP) | Bergeron, John (BP) | Cedillo, Gerardo (BP) | Mousavi, Maryam (BP) | Pineda, Wilson (BP) | Soza, Eric (BP) | Le, Fei (Baker Hughes, a GE Company) | Maurer, Hans-Martin (Baker Hughes, a GE Company) | Mirto, Ettore (Schlumberger) | Sun, Keli (Schlumberger)
Copyright 2019 held jointly by the Society of Petrophysicists and Well Log Analysts (SPWLA) and the submitting authors. Annual Logging Symposium held in The Woodlands, TX, USA June 17-19, 2019. ABSTRACT Typically, only conventional logging while drilling (LWD) resistivity and gamma ray logs are acquired in overburden sections of deep-water wells. Very important decisions impacting drilling safety and borehole stability must be made based on correct and timely interpretation of these logs. Drilling-induced fractures, faults, and eccentricity effects in large holes drilled with oil-based mud are common reasons for anomalous responses of LWD resistivity tools in overburden sections. These anomalies are often associated with fluid losses and other drilling hazards such as borehole assembly sticking. With the limited number of real-time (RT) measurements even if the optimal minimal set of RT curves is selected, the interpretation of these anomalies is challenging. Drilling-induced fractures can be misinterpreted as eccentricity or even as a permeable zone with resistive invasion in water sands or with a hydrocarbon-bearing layer, which is especially important for proper casing and cementing decisions. Resistivity modelling is an irreplaceable tool that enables us to uniquely identify the cause of each anomaly. Time-lapse measurements also help to recognize and identify the causes of anomalies as borehole conditions change with time. Fractures can become deeper with continued overbalance or healed with lost-circulation material or a reduction of equivalent circulating density. Washouts typically enlarge with time and after reaming. We present several case studies from deep-water wells in the Gulf of Mexico illustrating typical LWD resistivity anomalies in overburden sections. The examples include fault identification and borehole events such as fluid losses, borehole enlargement, and gas-bearing intervals. The challenges of interpreting each anomaly and the necessity of the appropriate LWD resistivity modeling kit are clearly demonstrated. Many of the examples illustrate the advantages of measuring after drilling (MAD pass) logs. INTRODUCTION When drilling overburden sections in deep water wells, the hole diameters are big, open hole sections are long and, typically, the LWD suite is limited to conventional resistivity and gamma ray (GR) logs. Additionally, the limited number of real-time (RT) resistivity curves makes the unique interpretation of resistivity data difficult.