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permeability
Investigating the causes of permeability anisotropy in heterogeneous conglomeratic sandstone using multiscale digital rock
Chi, Peng (China University of Petroleum (East China), China University of Petroleum (East China)) | Sun, Jianmeng (China University of Petroleum (East China), China University of Petroleum (East China)) | Yan, Weichao (Ocean University of China, Ocean University of China) | Luo, Xin (China University of Petroleum (East China), China University of Petroleum (East China)) | Ping, Feng (Southern University of Science and Technology)
Heterogeneous conglomeratic sandstone exhibits anisotropic physical properties, rendering a comprehensive analysis of its physical processes challenging with experimental measurements. Digital rock technology provides a visual and intuitive analysis of the microphysical processes in rocks, thereby aiding in scientific inquiry. Nevertheless, the multiscale characteristics of conglomeratic sandstone cannot be fully captured by a single-scale digital rock, thus limiting its ability to characterize the pore structure. Our work introduces a proposed workflow that employs multiscale digital rock fusion to investigate permeability anisotropy in heterogeneous rock. We utilize a cycle-consistent generative adversarial network (CycleGAN) to fuse CT scans data of different resolutions, creating a large-scale, high-precision digital rock that comprehensively represents the conglomeratic sandstone pore structure. Subsequently, the digital rock is partitioned into multiple blocks, and the permeability of each block is simulated using a pore network. Finally, the total permeability of the sample is calculated by conducting an upscaling numerical simulation using the Darcy-Stokes equation. This process facilitates the analysis of the pore structure in conglomeratic sandstone and provides a step-by-step solution for permeability. From a multiscale perspective, this approach reveals that the anisotropy of permeability in conglomeratic sandstone stems from the layered distribution of grain sizes and differences in grain arrangement across different directions.
- Europe > Norway > North Sea > Central North Sea > Utsira High > PL 338 > Block 16/1 > Edvard Grieg Field > Åsgard Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Utsira High > PL 338 > Block 16/1 > Edvard Grieg Field > Skagerrak Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Utsira High > PL 338 > Block 16/1 > Edvard Grieg Field > Hegre Formation (0.99)
- (3 more...)
Petroleum Engineering, University of Houston, 2. Metarock Laboratories, 3. Department of Earth and Atmospheric Sciences, University of Houston) 16:00-16:30 Break and Walk to Bizzell Museum 16:30-17:30 Tour: History of Science Collections, Bizzell Memorial Library, The University of Oklahoma 17:30-19:00 Networking Reception: Thurman J. White Forum Building
- Research Report > New Finding (0.93)
- Overview (0.68)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Mineral (0.72)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.68)
- (2 more...)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.93)
- North America > United States > California (1.00)
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- Asia (1.00)
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- Geology > Structural Geology > Tectonics > Plate Tectonics (1.00)
- Geology > Rock Type (1.00)
- Geology > Mineral (1.00)
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- Geophysics > Gravity Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Passive Seismic Surveying (0.92)
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- Materials > Metals & Mining (1.00)
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- Government > Regional Government > North America Government > United States Government (1.00)
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- North America > United States > Nevada > Dixie Valley Field (0.99)
- North America > United States > California > Mayacamas Mountains > Geysers Field (0.99)
- North America > Trinidad and Tobago > Trinidad > Southern Basin (0.99)
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- Information Technology > Modeling & Simulation (0.92)
- Information Technology > Communications > Collaboration (0.40)
Simultaneous prediction of the geofluid and permeability of reservoirs in prestack seismic inversion
Yang, Wenqiang (Laoshan Laboratory, China University of Petroleum (East China), Pilot National Laboratory for Marine Science and Technology (Qingdao)) | Zong, Zhaoyun (Laoshan Laboratory, China University of Petroleum (East China), Pilot National Laboratory for Marine Science and Technology (Qingdao)) | Sun, Qianhao (Laoshan Laboratory, China University of Petroleum (East China), Pilot National Laboratory for Marine Science and Technology (Qingdao))
ABSTRACT Geofluid discrimination and permeability prediction are indispensable steps in reservoir evaluation. From the perspective of prestack seismic inversion, predicting fluid indicators is an effective method for obtaining fluid properties directly from seismic data. In contrast, the direct prediction of permeability from observed seismic gathers is constrained by the difficulty in establishing a link between permeability and elastic parameters. However, we show that the pore structure parameters in seismic petrophysical theory are highly related to permeability, providing a new solution for predicting permeability using seismic data. Therefore, the correlation between the shear flexibility factor and permeability is first verified based on logging curves and laboratory data, and the results demonstrate that the shear flexibility factor can be an indicator of reservoir permeability. Second, an approximate reflection coefficient equation is derived for the direct characterization of the shear flexibility factor. In the developed equation, a novel fluid indicator, expressed as the ratio of Russell’s fluid indicator to the square of the shear flexibility factor, is defined for the simultaneous prediction of fluid types and permeability. With the validated response of the novel fluid indicator to geofluid types, we achieve simultaneous predictions of fluid types and reservoir permeability characteristics from prestack seismic data, using a boundary-constrained Bayesian inversion strategy. The model tests and the application of field data from a clastic reservoir confirm the effectiveness and applicability of the method.
- Asia > China > Sichuan Province (0.28)
- North America > United States > Texas (0.28)
- Research Report > New Finding (0.34)
- Research Report > Experimental Study (0.34)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.69)
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
- Oceania > New Zealand > North Island > Taranaki Basin (0.99)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- (24 more...)
Upon completing this Learning Module assignment, the participant should be able to define the following reservoir properties and understand their importance in the overall reservoir development scheme: rock properties (porosity, permeability, fluid saturation, compressibility, anisotropy), fluid properties (phase behavior, PVT relationships, density, viscosity, compressibility, formation volume factor, gas-oil ratio), rock/fluid interactions (wettability, interfacial tension, capillary pressure, relative permeability), read and understand wellsite descriptions of recovered core material, evaluate the core handling and preservation techniques employed, and select sample intervals for laboratory analysis, generate a procedure for preparing and analyzing selected core samples, specifying the tests to be run and the information to be obtained, describe the laboratory techniques and perform the calculations used for determining rock properties, design procedures for obtaining representative surface and subsurface formation fluid samples, describe procedures for generating PVT analyses of reservoir fluid samples, and interpret the resulting reports, and use published correlations to estimate reservoir fluid properties.
- North America > United States > Texas (1.00)
- Europe (0.93)
- Research Report > New Finding (0.93)
- Overview (0.88)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.68)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.47)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.93)
The most important data for designing a fracture treatment are the in-situ stress profile, formation permeability, fluid-loss characteristics, total fluid volume pumped,propping agent type and amount, pad volume, fracture-fluid viscosity, injection rate, and formation modulus. It is very important to quantify the in-situ stress profile and the permeability profile of the zone to be stimulated, plus the layers of rock above and below the target zone that will influence fracture height growth. There is a structured method that should be followed to design, optimize, execute, evaluate, and reoptimize the fracture treatments in any reservoir. The first step is always the construction of a complete and accurate data set.Table 1 lists the sources for the data required to run fracture propagation and reservoir models. The design engineer must be capable of analyzing logs, cores, production data, and well-test data and be capable of digging through well files to obtain all the information needed to design and evaluate the well that is to be hydraulically fracture treated.
- Information Technology > Knowledge Management (0.40)
- Information Technology > Communications > Collaboration (0.40)
Evaluation of Fracture Stimulation Performance Based on Production Log Interpretation
Li, Gongsheng (Harold Vance Department of Petroleum Engineering, Texas A&M University, College Station, Texas) | Sakaida, Shohei (Chevron Corp) | Zhu, Ding (Harold Vance Department of Petroleum Engineering, Texas A&M University, College Station, Texas) | Hill, A. D. (Harold Vance Department of Petroleum Engineering, Texas A&M University, College Station, Texas) | Kerr, Erich (SM Energy Company, Houston, Texas)
Abstract Production logging is a traditional approach to monitor an inflow profile in a hydraulically fractured horizontal well. By quantitatively interpreting production logs run in a hydraulically fractured horizontal well, we can evaluate and optimize fracture stimulation design. This paper presents a field example of completion analysis based on production log interpretation. In this well, the fracture treatment design was varied by stage to examine which completion parameters are more influential on productivity. The production performance for each stage was evaluated using an array of spinner flowmeters and phase holdup-sensing devices, and a temperature log. Multiple-sensor array tools were used to measure the phase holdup and local fluid velocity along the wellbore. The cross-sectional area of the wellbore was divided into five segments. The phase distribution of gas, oil, and water within each wellbore segment was assigned based on the phase holdup values along the wellbore. The array spinner flowmeters provided the local velocity inside each wellbore segment. This paper presents a methodology for using data from array production logging tools to interpret the volumetric flow rates of each phase at each interpretation point along the wellbore. The differences in these wellbore phase flow rates provide the downhole inflow distribution along the hydraulically fractured horizontal well. Temperature logs can reveal fluid entry locations as the places where temperature anomalies caused by Joule-Thomson effects occur. When gas is produced, the Joule-Thomson cooling effect as the gas expands in the near-well region generates a cool anomaly that locates the gas entry location. In some cases, the Joule-Thomson heating effect caused by liquid production identifies liquid inflow locations. By performing a temperature history match using a thermal simulator, we quantitatively obtained the gas inflow rates at each active cluster location. This paper demonstrates that the temperature log interpretation provides the inflow profile along the experimental well based on the cooling anomalies. Once we confirm that the inflow profiles estimated by the interpretation of the array production logging tool and the temperature log are comparable to each other, we evaluate the fracture stimulation design based on the production performance for each stage. We present the effect of statistically significant fracture design variables on stage production performance derived from the production log interpretations.
- North America > United States > Texas (1.00)
- Asia (0.68)
- North America > United States > West Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- North America > United States > Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Eagle Ford Shale Formation (0.99)
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_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 216856, “ML-Driven Integrated Approach for Perforation Interval Selection Based on Advanced Borehole-Image AI-Assisted Interpretation,” by Alexander Petrov, SPE, Mounir Belouahchia, SPE, and Abdelwahab Noufal, SPE, ADNOC. The paper has not been peer reviewed. _ In the complete paper, the authors propose an artificial-intelligence (AI)-assisted work flow that uses machine-learning (ML) techniques to identify sweet spots in carbonate reservoirs. This process involves annotation of geologic features using a well database, with supervision from subject-matter experts (SMEs). The resulting ML model is tested on new wells and can identify pay zones, perforation intervals, and stress analysis. The models successfully detect fractures, breakouts, bedding planes, vugs, and slippage passages with pixel-level precision, reducing borehole-image (BHI) analysis time. BHI Interpretation and Preprocessing The use of BHIs requires manual interpretation and data identification, heavily relying on the expertise and time of SMEs. A widely adopted approach to address this challenge is the use of supervised computer-vision algorithms, a subfield of AI. These algorithms optimize the task function or model based on examples they have learned from data during training. However, when applied to BHIs, certain ML challenges must be addressed, including the following: - Detecting features in wells from different reservoirs using a model trained on wells from one reservoir can be highly challenging because reservoirs may exhibit distinct geological characteristics. - The handling of parts of BHIs with missing data, depicted by vertically slanted white strips, poses considerable difficulty. Therefore, the authors created a deep-learning approach based on a generative adversarial network architecture to fill the gaps automatically (Fig. 1). - The labels provided by geologists often do not have pixelwise precision, causing the machine to become confused while trying to learn inconsistent patterns. The authors use a convolutional neural network (CNN) to compute a probability map for pixels belonging to specific classes. In this application, a class is defined as any of the heterogeneities in the BHI; however, this method is applicable to any type of heterogeneities in an image. After training, the CNN module provides the optimal probability for each pixel in the image. To classify regions in the BHI based on heterogeneities, a class-specific threshold is established. Pixels with values above the thresholds are assigned to the corresponding class, while those below the thresholds are assigned to the background. BHI-Derived Porosity A new approach for borehole-derived porosity was developed in-house to overcome the limitations of existing techniques widely used in the industry. This approach capitalizes on BHIs for multiple analyses, including structural dip assessment, fault and fracture identification, and determination of minimum and maximum horizontal stress orientation. However, its primary strength lies in quantifying the fraction of secondary porosity in heterogeneous or dual-porosity carbonate formations. The authors have devised a novel method that uses borehole electrical images to conduct porosity and image connectivity analysis. By implementing this technique, essential information can be extracted regarding the spatial distribution of porosity and the extent of secondary porosity fraction.
- Geology > Geological Subdiscipline > Stratigraphy (0.56)
- Geology > Geological Subdiscipline > Geomechanics (0.35)
The scientific research process begins as one tries to find explanations for a phenomenon. We make observations, define the problem statement, and review the existing domains of research that could be used. Another approach is to explore theoretical problems, those that are purely conceptual at present but provide a solution when a related observation is made in the future. Though these approaches sound isolated, both are part of characterizing uncertainty, and uncertainty comes in all scales and dimensions. This challenges us to learn at all scales possible, from the fume hoods in the laboratory to magnificently exposed outcrops and through deep narrow boreholes that drill through subsurface reservoirs. The combined efforts often convert learnings to actionable intelligence. At a smaller scale, porosity and permeability are probably the two most-studied rock properties among those that have meaningful implications for hydrocarbon reservoirs. Paper SPE 216856 considers machine-learning (ML) methods for classifying reservoir texture at a microscale. Borehole-image logs long have been used to obtain a picture of subsurface reservoirs. Unfortunately, a majority of the observations are qualitative. Quantifying these features faces the challenge of continuity, upscaling, and regional correlation. As we explore the latitude of ML-based applications, the use of these techniques for quantifying image logs becomes very relevant. The authors of that paper contribute to quantifying textural features at a “fume-hood scale” and develop a work flow with the potential for estimating properties such as porosity and permeability from a different domain of reservoir characterization. I often wonder how much the domain on formation evaluation encompasses. While geoscience-driven reservoir characterization is a big part of it, how reservoirs change over time also is a complementary observation. Paper URTeC 3864861 discusses various aspects of geomechanical changes that a hydraulically fractured reservoir goes through during its life cycle. The authors here study the relationship between measured strain from the fiber-optic sensors and wellhead pressure. Research like this could be extended to predicting production profiles and estimating recovery factors, which are important considerations in designing a stimulation program for sustaining production, maximizing recovery, and improving financial matrices for the capital program. I believe information could be categorized as learning, knowledge, and intelligence. Any scientific process starts with set of careful observations bound by an envelope of hypotheses. This is learning. Learning, which could be verified by predictable and repeatable outcomes from carefully designed experiments of complementing domains, becomes knowledge. Actionable knowledge, which then could be used to alter an outcome or a process, becomes intelligence. Paper URTeC 3871303 discusses a development strategy in a restrictive development unit with an existing parent well. Here, considerations are heavily weighted toward optimizing both interwell spacing and capital efficiency. The search for answers to a problem like this must seek guidance from a variable-scale experiment. The study here establishes the big picture with the structural elements of the basin that could restrict both the continuity of the reservoir and the nature of the producible fluid. With this framework, the model is then set to iterate from several different perspectives. Potential interwell communications are explored by measuring fracture-driven interactions (FDIs) and quantifying stimulated reservoir volume. What is impressive here is the different domains from which the authors seek answers. Direct observations from acoustic-fiber measurements for FDI and the geochemistry of produced fluids for identifying unique signatures from vertically separated formations are individual domains that seek the same answers in various scales. The study recommends the optimal spacing between wells and a stimulation design that minimizes well interference, reduces competition for resources between wells, and avoids overcapitalizing the program. This is how knowledge transforms into intelligence. I hope the readers appreciate the scales of characterization in these three papers. As a student of geology, I have always been fascinated by the concept of scale and its relation to the domains of science that we deal with. Unlike general relativity and quantum mechanics, most geologic phenomena are observed in all scales. It is just the uncertainty that needs to be quantified.