Results
This study aimed to optimize hydrocarbon production from the naturally fractured reservoirs in the VMM-1 gas field by identifying and interpreting the fault and fracture systems. To achieve this, deep learning fault segmentation was integrated with HTI analysis and ambient microseismic recording. The fault pattern was studied using deep learning fault segmentation, while HTI analysis highlighted the magnitude and distribution of fractures. Ambient microseismic recording was used to identify active faults and fractures. By integrating these three methods, we were able to understand the direction, density, and effectiveness of the various fracture systems, as well as the lateral extent and continuity of the Rosa Blanca Formation. This integration of methods was essential in maximizing ultimate recovery and economic success and has potential applications in the development of other naturally fractured reservoirs.
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- Asia > Middle East (0.67)
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- Phanerozoic > Cenozoic > Neogene (0.68)
- Phanerozoic > Mesozoic > Jurassic (0.68)
- Geology > Structural Geology > Tectonics > Plate Tectonics (1.00)
- Geology > Structural Geology > Tectonics > Compressional Tectonics > Fold and Thrust Belt (1.00)
- Geology > Structural Geology > Fault > Dip-Slip Fault (1.00)
- (3 more...)
- Well Completion > Hydraulic Fracturing (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Naturally-fractured reservoirs (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Neural networks (1.00)
Abstract Multistage hydraulic fracturing is essential to unlock the potential of unconventional reservoirs and produce them economically. Data acquisition technologies, such as Distributed Acoustic Sensing (DAS), have been revolutionized in the last decade for real-time downhole monitoring of hydraulic fracturing jobs providing invaluable information related to stimulation and completion efficiency. The primary objective of this work is to utilize an integrated artificial intelligence (AI) assisted workflow that incorporates the field data acquired from different sources into physics-based fracture propagation model, which can automatically calibrate the uncertain input parameters, quantify the associated uncertainties, and ultimately provide more reliable fracture geometries. The AI-assisted workflow incorporates the obtained data from different sources to a hydraulic fracturing simulator. The framework starts with identifying the uncertain parameters that have significant impact on the target objectives. These objectives consist of surface treating pressures, in-well fluid distributions across the clusters obtained from the installed high-frequency DAS (HF-DAS) in the treatment well, and cross-well fracture hits that are characterized by the installed low-frequency DAS (LF-DAS) in the monitoring well. The target objectives are simultaneously and automatically matched through the calibration of hydraulic fracturing simulator by developing highly efficient and accurate machine learning (proxy) models, which are integrated with a multi-proxy-based Markov Chain Monte Carlo (MCMC) algorithm to generate the history matching solutions and posterior distributions of the uncertain parameters that quantify the uncertainty of the resultant fracture geometry and assess the stimulation/completion efficiency in the treatment well. The established workflow is applied to a treatment well that consists of 19 fracturing stages, which demonstrates its capability in handling a problem that exhibits high-dimensionality and multiple objectives by automatically matching all objectives successfully for all the stages. The developed proxy models have high predictability and generalizability and are used in conjunction with MCMC to generate the history matching solutions. The generated solutions aid in diagnosing the stages that have low cluster efficiency and poor fluid distribution by investigating the posterior of the related completion parameter such as perforation diameter if the stage suffers from perforations erosion. The calibrated parameters and fracture geometries can then be used to optimize the well spacing, completion design, pumping schedule, fracturing fluids and proppants to achieve desirable results. This work emphasizes on the importance of information obtained from fracture diagnostic techniques to be incorporated into the physics-based models by presenting a systematic hybrid approach of data and physics that leads to a better understanding of fracture propagation, and subsequently maximizing well production.
- North America > United States > Texas (1.00)
- Europe (1.00)
- Asia > Middle East (0.67)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.46)
- Geology > Petroleum Play Type > Unconventional Play > Shale Play (0.46)
- 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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- Well Completion > Hydraulic Fracturing > Fracturing materials (fluids, proppant) (1.00)
- Well Completion > Completion Installation and Operations > Perforating (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Naturally-fractured reservoirs (1.00)
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- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
ABSTRACT: The extraction of 3D block structure from a point cloud is a nontrivial but important task in rock engineering. Block in Point Cloud (BLOCKinPCD) is a novel processing system that converts the point cloud data of rock outcrops into 3D rock structure. The workflow and processing functions include: (1) spatially identifying and extracting the point clouds of each discontinuity set; (2) quantifying the geometric parameters of each set (orientation, spacing and persistence); (3) characterizing the in-situ block system by assembling the block-forming sets into discrete fracture network; (4) quantifying the volume distribution of the block system and (5) evaluating the stability of blocks intersecting the excavation surfaces. As shown herein, BLOCKinPCD has been successfully applied to outcrops exposed in rock slopes and tunnel excavations. INTRODUCTION One of the challenges in rock engineering relates to the spatial variation of rock structure, which cannot always be characterized in the investigation and design stages. Often, as in the case of tunnel excavation, rock structure details are only revealed during construction. Once the rock is exposed by tunneling, joint traces can be mapped, as a basis for quantifying the near-surface 3D block structure. In addition, the behavior of the in-situ block system after the excavation is the basis for judging whether to update the construction and support plan. Traditional scanline surveys provide information pertaining to the frequency of discontinuities, normally over a range of tens of meters. The related processing methods introduced more than 40 years ago (Hudson & Priest 1983) provide estimates of the full range of discontinuity frequency variation, including the directional magnitudes of the maximum and minimum frequencies. The trace length estimation (Pahl 1981) based on window mapping provides a foundation for inferring the area size of discontinuities in 3D space. Fracture system modeling (Dershowitz & Einstein 1998) is a methodology developed to statistically combine geometric characteristics of shape, size, location and orientation of discontinuities as a computer model of the fracture network at a particular location.
- Well Completion > Hydraulic Fracturing (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Naturally-fractured reservoirs (0.76)
- Information Technology > Cloud Computing (0.61)
- Information Technology > Artificial Intelligence > Machine Learning (0.48)
Abstract Many geo-energy related applications involve predicting the behavior of fluid flow in fractured subsurface reservoirs. Naturally fractured carbonate reservoirs are particularly important for being a major source of the world's hydrocarbon production. These reservoirs are also currently being considered as potential CO2 storage sites that will support net zero emissions goal. Simulation of flow in fractured reservoirs is a challenging task that typically involves upscaling the effective permeability of the fracture network and matrix into continuum models that consider the reservoir scale. The most accurate way to obtain such upscaled permeability for fracture networks is to perform single-phase flow simulations in statistical realizations of the fracture network using three-dimensional unstructured grids and explicit modelling of fractures. This step can be computationally challenging for highly dense fracture networks due to the difficulty in meshing the fractures and the rock matrix. Here, we present a method to reduce the complexity of the fracture network while still preserving the behavior of its effective permeability. Our approach involves a fracture merging algorithm that reduces the number of fractures allowing for faster meshing and upscaling. The fracture merging algorithm uses three different similarity metrics: fracture orientation, fracture area and distance between fractures. These metrics are used to identify similar fractures that can be merged into one single fracture with increased permeability. The upscaling algorithm to obtain the effective permeability of a grid cell containing a fracture network relies on flow simulations in three-dimensional unstructured meshes. We applied our method to different sub-networks extracted from a stochastically generated fracture network of a Brazilian Pre-Salt carbonate reservoir. We found that the average permeability of all fractures of the resulting fracture network increases with merging intensity, i.e., with decreasing the number of fractures, while the resulting upscaled effective permeability for the network remains in the same order of magnitude. This shows that the flow-based upscaling workflow including the merging algorithm leads to a significant reduction of complexity of fracture networks and consequently their 3D unstructured meshes while maintaining the structural and topological features that account for the fracture network effective permeability. Our proposed method is simple to implement and relies only on geometrical properties of the fractures. Other machine-learning based models have been proposed to achieve similar simplification of fracture networks, however, they are not easily incorporated into existing reservoir simulation tools and codes like the method presented in this work. Moreover, such previously published approaches do not consider flow in matrix and thus haven't been tested in scenarios where the matrix also contributes to flow.
- Europe > United Kingdom > England (0.28)
- Europe > Austria (0.28)
- Well Completion > Hydraulic Fracturing (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Naturally-fractured reservoirs (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation > Scaling methods (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Information Technology > Software (0.68)
- Information Technology > Modeling & Simulation (0.49)
- Information Technology > Artificial Intelligence > Machine Learning (0.48)
A Hybrid Embedded Discrete Fracture Model and Dual-Porosity, Dual-Permeability Workflow for Hierarchical Treatment of Fractures in Practical Field Studies
Hui, Mun-Hong (Chevron Technical Center, a division of Chevron USA Inc (Corresponding author)) | Mallison, Bradley (Chevron Technical Center, a division of Chevron USA Inc) | Thomas, Sunil (Chevron Technical Center, a division of Chevron USA Inc) | Muron, Pierre (Chevron Technical Center, a division of Chevron USA Inc) | Rousset, Matthieu (Chevron Technical Center, a division of Chevron USA Inc) | Earnest, Evan (Chevron Technical Center, a division of Chevron USA Inc) | Playton, Ted (Chevron Technical Center, a division of Chevron USA Inc) | Vo, Hai (Chevron Technical Center, a division of Chevron USA Inc) | Jensen, Clair (Chevron Technical Center, a division of Chevron USA Inc)
Summary Natural fracture systems comprise numerous small features and relatively few large ones. At field scale, it is impractical to treat all fractures explicitly. We represent the largest fractures using an embedded discrete fracture model (EDFM) and account for smaller ones using a dual-porosity, dual-permeability (DPDK) idealized representation of the fracture network. The hybrid EDFM + DPDK approach uses consistent discretization schemes and efficiently simulates realistic field cases. Further speedup can be obtained using aggregation-based upscaling. Capabilities to visualize and post-process simulation results facilitate understanding for effective management of fractured reservoirs. The proposed approach embeds large discrete fractures as EDFM within a DPDK grid (which contains both matrix and idealized fracture continua for smaller fractures) and captures all connections among the triple media. In contrast with existing EDFM formulations, we account for discrete fracture spacing within each matrix cell via a new matrix-fracture transfer term and use consistent assumptions for classical EDFM and DPDK calculations. In addition, the workflow enables coarse EDFM representations using flow-based cell-aggregation upscaling for computational efficiency. Using a synthetic case, we show that the proposed EDFM + DPDK approach provides a close match of simulation results from a reference model that represents all fractures explicitly, while providing runtime speedup. It is also more accurate than previous standard EDFM and DPDK models. We demonstrate that the matrix-fracture transfer function agrees with flow-based upscaling of high-resolution fracture models. Next, the automated workflow is applied to a waterflooding study for a giant carbonate reservoir, with an ensemble of stochastic fracture realizations. The overall workflow provides the computational efficiency needed for performance forecasts in practical field studies, and the 3D visualization allows for the derivation of insights into recovery mechanisms. Finally, we apply a finite-volume tracer-based flux post-processing scheme on simulation results to analyze production allocation and sweep for understanding expected waterflood performance.
- North America > United States > Texas (1.00)
- Europe (1.00)
- Asia > Middle East (0.67)
- Geology > Geological Subdiscipline > Geomechanics (0.93)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.67)
- Well Completion > Hydraulic Fracturing (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Naturally-fractured reservoirs (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation > Scaling methods (1.00)
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Understanding the mechanisms of wave-induced fluid flow in the pore space combined with attenuation and dispersion measurements may be used to estimate rock hydraulic properties. Including macroscopic, mesoscopic, and local/squirt flow, a systematic treatment of attenuation and dispersion mechanisms is presented.
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- North America > United States > Texas (0.92)
- Summary/Review (1.00)
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- Geology > Rock Type > Sedimentary Rock > Clastic Rock (1.00)
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- Geology > Structural Geology > Tectonics > Plate Tectonics > Earthquake (0.67)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (1.00)
- Geophysics > Borehole Geophysics (1.00)
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- North America > United States > West Virginia > Appalachian Basin > Berea Sandstone Formation (0.97)
- North America > United States > Pennsylvania > Appalachian Basin > Berea Sandstone Formation (0.97)
- North America > United States > Ohio > Appalachian Basin > Berea Sandstone Formation (0.97)
- North America > United States > Kentucky > Appalachian Basin > Berea Sandstone Formation (0.97)
- Well Completion > Hydraulic Fracturing (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Naturally-fractured reservoirs (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation > Scaling methods (1.00)
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Abstract Chemistry and the materials portfolio play a central role in fracturing success. Challenges in a achieving successful and productive proppant pack are resolved using varying products and fit-for-purpose chemistry utilization. The conventional understanding of using fluid loss additive (FLA) only for reducing fluid leakoff in high-permeability formations can be extended with advanced digital tools for optimum realization of a broader application spectrum for well-specific challenges. Polylactic acid-based powdered degradable FLA (DFLA) was developed with engineered particle size distribution to plug rock pore throats. Core flow tests were conducted with and without DFLA with borate crosslinked base fluid to measure the performance metrics. The application spectrum was extended beyond the fluid loss control in high-permeability rock to aid in screening out multiple fractures and natural fractures, reducing poroelastic tendency of tight, tectonically influenced formations. An advanced numerical modeling simulation approach was used to evaluate the distribution of the DFLA particles along the fracture cross-section and their dynamics to yield optimum fracture geometry with lesser pad volume. Coreflood tests demonstrated a reduction of up to 40% in fluid loss coefficient and spurt loss components with 30 lbm/1,000 gal DFLA loading. A regained permeability reduction of 12% from the baseline was observed when 25% particulate DFLA mass loss occurred, which can be minimized with higher shut-in times and complete degradation. The spectrum was expanded conceptually for up to eight applications based on literature references. Digitally advanced hydrodynamics and an in situ kinetics simulator were used to accurately model the slurry flow with and without DFLA. The model was extended with a sensitivity study with 32 synthetic cases to extend FLA utilization to medium- and low-efficiency formations. The modeling results showed that more than 50% of crosslinked pad volume could be saved while retaining the same fracture geometry evolution. Industry use of FLA chemistry has been minimal. In the digital age, this is the first and a unique demonstration of how digital tools can aid extending the material portfolio spectrum investigated from laboratory, simulation, and field case perspectives. Multiple applications of FLA can enhance project economics and reduce polymer and fracturing fluid formation damage by lowering the difference between differential pressure at the fracture face and drawdown during cleanup and the production phase.
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- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.46)
- North America > United States > South Dakota > Williston Basin (0.99)
- North America > United States > North Dakota > Williston Basin (0.99)
- North America > United States > Montana > Williston Basin (0.99)
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- Information Technology > Artificial Intelligence > Machine Learning (0.93)
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Abstract The main objective of this study is to perform Uncertainty Quantification (UQ) using a detailed representation of fractured reservoirs. This is achieved by creating a simplified representation of the fracture network while preserving the main characteristics of the high-fidelity model. We include information at different scales in the UQ workflow which allows for a large reduction in the computational time while converging to the high-fidelity full ensemble statistics. Ultimately, it allows us to make accurate predictions on geothermal energy production in highly heterogeneous fractured porous media. The numerical reservoir simulation tool we use in this work is the Delft Advanced Research Terra Simulator (DARTS). It is based on Finite Volume approximation in space, fully coupled explicit approximation in time, and uses the novel linearization technique called Operator-Based Linearization (OBL) for the system of discretized nonlinear governing equations. We use a fracture network generation algorithm that uses distributions for length, angles, size of fracture sets, and connectivity as its main input. This allows us to generate a large number of complex fracture networks at the reservoir scale. We developed a pre-processing algorithm to simplify the fracture network and use graph theory to analyze the connectivity before and after pre-processing. Furthermore, we use metric space modeling methods for statistical analysis. A robust coarsening method for the Discrete Fracture Matrix model (DFM) is developed which allows for great control over the degree of simplification of the network’s topology and connectivity. We apply the framework to modeling of geothermal energy extraction. The pre-processing algorithm allows for a hierarchical representation of the fracture network, which in turn is utilized in the reduced UQ methodology. The reduced UQ workflow uses the coarser representation of the fracture networks to partition/rank the high-fidelity parameter space. Then a small subset of high-fidelity models is chosen to represent the full ensemble statistics. Hereby, the computational time of the UQ is reduced by two/three orders of magnitude, while converging to similar statistics as the high-fidelity full ensemble statistics. The methods developed in this study are part of a larger project on a prediction of energy production from carboniferous carbonates. The final goal is to perform UQ in pre-salt reservoirs which are characterized by complex reservoir architecture (i.e., large karstification and fracture networks). The UQ of fractured reservoirs is typically done in the petroleum industry using effective media models. We present a methodology that can efficiently handle a large ensemble of DFM models, which represent complex fracture networks and allow for making decisions under uncertainty using more detailed high-resolution numerical models.
- South America > Brazil (0.46)
- Europe > Netherlands > South Holland > Delft (0.25)
- Geology > Geological Subdiscipline > Geomechanics (0.46)
- Geology > Structural Geology > Tectonics > Salt Tectonics (0.34)
- Geology > Rock Type > Sedimentary Rock (0.34)
- Well Completion > Hydraulic Fracturing (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Naturally-fractured reservoirs (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning (0.88)
- Information Technology > Artificial Intelligence > Cognitive Science > Problem Solving (0.54)
Abstract Upscaling of discrete fracture networks to continuum models such as the dual porosity/dual permeability (DPDP) model is an industry-standard approach in modelling of fractured reservoirs. While flow-based upscaling provides more accurate results than analytical methods, the application of flow-based upscaling is limited due to its high computational cost. In this work, we parametrize the fine-scale fracture geometries and assess the accuracy of several convolutional neural networks (CNNs) to learn the mapping between this parametrization and the DPDP model closures such as the upscaled fracture permeabilities and the matrix-fracture shape factors. We exploit certain similarities between this task and the problem of image classification and adopt several best practices from the state-of-the-art CNNs used for image classification. By running a sensitivity study, we identify several key features in the CNN structure which are crucial for achieving high accuracy of predictions for the DPDP model closures, and put forward the corresponding CNN architectures. Obtaining a suitable training dataset is challenging because i) it requires a dedicated effort to map the fracture geometries; ii) creating a conforming mesh for fine-scale simulations in presence of intersecting fractures typically leads to bad quality mesh elements; iii) fine-scale simulations are time-consuming. We alleviate some of these difficulties by pre-training a suitable CNN on a synthetic random linear fractures’ dataset and demonstrate that the upscaled parameters can be accurately predicted for a realistic fracture configuration from an outcrop data. The accuracy of the DPDP results with the predicted model closures is assessed by a comparison with the corresponding fine-scale discrete fracture-matrix (DFM) simulation of a two-phase flow in a realistic fracture geometry. The DPDP results match well the DFM reference solution, while being significantly faster than the latter.
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- North America > United States > Texas (0.28)
- Well Completion > Hydraulic Fracturing (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Naturally-fractured reservoirs (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation > Scaling methods (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
Abstract One of the major challenges facing rock engineers is that of establishing the bulk properties of the fractured rock mass on which or in which they are working. These are controlled principally by the geometry of the fracture network and the properties of the individual fractures. The network is built up by the superposition of separate fracture sets, each related to a geological event (burial tectonism and exhumation). In structural geology ‘fracture analysis’ is used to determine the order in which the sets are superimposed and knowing this, the 3D geometry of the network can be determined. Examination of the fracture surfaces can also reveal whether they are shear or extensional. Provided with this information the rock engineer can then combine it with site specific tests on the properties of the individual fracture sets and begin to quantify the likely physical behaviour of rock masses on an engineering scale. This paper presents a brief introduction to the concepts of fracture analysis, and goes on to show how these can usefully by integrated with typical rock mechanics analyses to give improved data for rock engineering design. Introduction The rock masses in which rock engineering takes place are the result of various geological processes. Such processes and their products are well understood by geologists, and they use this understanding to interpret the genesis of a rock mass. Here, we explore how such understanding can bring benefits to rock engineering. We begin with a brief discussion of the geological processes that generate fractures in a rock during its cycle within the crust from (for sedimentary rocks) deposition, through burial and diagenesis, possible deformation linked to plate motion (orogenesis, i.e. mountain building) and finally exhumation to its present position at the Earth’s surface. Each of these processes is likely to result in the formation of a fracture set whose orientation and type (shear or extensional) will be determined by the stress field acting at the time. Crucially, the different types are likely to possess different mechanical properties.
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Structural Geology > Tectonics > Plate Tectonics (0.93)
- Geology > Structural Geology > Tectonics > Compressional Tectonics > Fold and Thrust Belt (0.34)
- Well Completion > Hydraulic Fracturing (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Naturally-fractured reservoirs (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Reservoir geomechanics (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.68)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.68)