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Abstract Borehole image logs are used to identify the presence and orientation of fractures, both natural and induced, found in reservoir intervals. The contrast in electrical or acoustic properties of the rock matrix and fluid-filled fractures is sufficiently large enough that sub-resolution features can be detected by these image logging tools. The resolution of these image logs is based on the design and operation of the tools, and generally is in the millimeter per pixel range. Hence the quantitative measurement of actual width remains problematic. An artificial intelligence (AI) -based workflow combines the statistical information obtained from a Machine-Learning (ML) segmentation process with a multiple-layer neural network that defines a Deep Learning process that enhances fractures in a borehole image. These new images allow for a more robust analysis of fracture widths, especially those that are sub-resolution. The images from a BHTV log were first segmented into rock and fluid-filled fractures using a ML-segmentation tool that applied multiple image processing filters that captured information to describe patterns in fracture-rock distribution based on nearest-neighbor behavior. The robust ML analysis was trained by users to identify these two components over a short interval in the well, and then the regression model-based coefficients applied to the remaining log. Based on the training, each pixel was assigned a probability value between 1.0 (being a fracture) and 0.0 (pure rock), with most of the pixels assigned one of these two values. Intermediate probabilities represented pixels on the edge of rock-fracture interface or the presence of one or more sub-resolution fractures within the rock. The probability matrix produced a map or image of the distribution of probabilities that determined whether a given pixel in the image was a fracture or partially filled with a fracture. The Deep Learning neural network was based on a Conditional Generative Adversarial Network (cGAN) approach where the probability map was first encoded and combined with a noise vector that acted as a seed for diverse feature generation. This combination was used to generate new images that represented the BHTV response. The second layer of the neural network, the adversarial or discriminator portion, determined whether the generated images were representative of the actual BHTV by comparing the generated images with actual images from the log and producing an output probability of whether it was real or fake. This probability was then used to train the generator and discriminator models that were then applied to the entire log. Several scenarios were run with different probability maps. The enhanced BHTV images brought out fractures observed in the core photos that were less obvious in the original BTHV log through enhanced continuity and improved resolution on fracture widths.
Abstract In this case study, we apply a novel fracture imaging and interpretation workflow to take a systematic look at hydraulic fractures captured during thorugh fracture coring at the Hydraulic Fracturing Test Site (HFTS) in Midland Basin. Digital fracture maps rendered using high resolution 3D laser scans are analyzed for fracture morphology and roughness. Analysis of hydraulic fracture faces show that the roughness varies systematically in clusters with average cluster separation of approximately 20' along the core. While isolated smooth hydraulic fractures are observed in the dataset, very rough fractures are found to be accompanied by proximal smoother fractures. Roughness distribution also helps understand the effect of stresses on fracture distribution. Locally, fracture roughness seems to vary with fracture orientations indicating possible inter-fracture stress effects. At the scale of stage lengths however, we see evidence of inter-stage stress effects. We also observe fracture morphology being strongly driven by rock properties and changes in lithology. Identified proppant distribution along the cored interval is also correlated with roughness variations and we observe strong positive correlation between proppant concentrations and fracture roughness at the local scale. Finally, based on the observed distribution of hydraulic fracture properties, we propose a conceptual spatio-temporal model of fracture propagation which can help explain the hydraulic fracture roughness distribution and ties in other observations as well.
Suarez-Rivera, Roberto (W. D. Von Gonten Laboratories) | Panse, Rohit (W. D. Von Gonten Laboratories) | Sovizi, Javad (Baker Hughes) | Dontsov, Egor (ResFrac Corporation) | LaReau, Heather (BP America Production Company, BPx Energy Inc.) | Suter, Kirke (BP America Production Company, BPx Energy Inc.) | Blose, Matthew (BP America Production Company, BPx Energy Inc.) | Hailu, Thomas (BP America Production Company, BPx Energy Inc.) | Koontz, Kyle (BP America Production Company, BPx Energy Inc.)
Abstract Predicting fracture behavior is important for well placement design and for optimizing multi-well development production. This requires the use of fracturing models that are calibrated to represent field measurements. However, because hydraulic fracture models include complex physics and uncertainties and have many variables defining these, the problem of calibrating modeling results with field responses is ill-posed. There are more model variables than can be changed than field observations to constrain these. It is always possible to find a calibrated model that reproduces the field data. However, the model is not unique and multiple matching solutions exist. The objective and scope of this work is to define a workflow for constraining these solutions and obtaining a more representative model for forecasting and optimization. We used field data from a multi-pad project in the Delaware play, with actual pump schedules, frac sequence, and time delays as used in the field, for all stages and all wells. We constructed a hydraulic fracturing model using high-confidence rock properties data and calibrated the model to field stimulation treatment data varying the two model variables with highest uncertainty: tectonic strain and average leak-off coefficient, while keeping all other model variables fixed. By reducing the number of adjusting model variables for calibration, we significantly lower the potential for over-fitting. Using an ultra-fast hydraulic fracturing simulator, we solved a global optimization problem to minimize the mismatch between the ISIPs and treatment pressures measured in the field and simulated by the model, for all the stages and all wells. This workflow helps us match the dominant ISIP trends in the field data and delivers higher confidence predictions in the regional stress. However, the uncertainty in the fracture geometry is still large. We also compared these results with traditional workflows that rely on selecting representative stages for calibration to field data. Results show that our workflow defines a better global optimum that best represents the behavior of all stages on all wells, and allows us to provide higher-confidence predictions of fracturing results for subsequent pads. We then used this higher confidence model to conduct sensitivity analysis for improving the well placement in subsequent pads and compared the results of the model predictions with the actual pad results.
Brinkley, Kourtney (Devon Energy) | Ingle, Trevor (Devon Energy) | Haffener, Jackson (Devon Energy) | Chapman, Philip (Devon Energy) | Baker, Scott (Devon Energy) | Hart, Eric (Devon Energy) | Haustveit, Kyle (Devon Energy) | Roberts, Jon (Devon Energy)
Abstract This case study details the use of Sealed Wellbore Pressure Monitoring (SWPM) to improve the characterization of fracture geometry and propagation during stimulation of inter-connected stacked pay in the South Texas Eagle Ford Shale. The SWPM workflow utilizes surface pressure gauges to detect hydraulically induced fracture arrivals athorizontal monitor locations adjacent to the stimulated wellbore (Haustveit et al. 2020). A stacked and staggered development in Dewitt County provided the opportunity to jointly evaluateprimary completion and recompletion efforts spanning three reservoir target intervals. Fivemonitor wells at varying distances across the unit were employed for SWPM during the stimulation of four wells. An operational overview, analysis of techniques, correlation with seismic attributes, image log interpretations, and fracture model calibration are provided. Outputs from this workflow allow for a refined analysis ofthe overall completion strategy. The high-density, five well monitor array recorded a total of 160 fracture arrivals at varying vertical and lateral distances, with far-field fracture arrivalsprovidingsignificant insight into propagation rates and geometry. Apronounced trend occurred in both arrival frequency and volumes pumped as monitor locations increased in distance from the treatment well. Specific to target zone isolation, it was identified that traversing vertically in section through a high stress interval yielded a 30% reduction inarrival frequency. An indirect relationship between horizontal distance and arrival frequency was also observed when monitoring from the same interval. A decrease in fracture arrivals from 70% down to 8% was realized as offset distance increased from 120 to 1,700 ft. The results from this study have proven to be instrumental in guiding interdisciplinary discussion. Assessing fracture geometry and propagation during stimulation, particularly in the co-development of a stacked pay reservoir, is paramount to the determination of proper completion volume, perforation design, and well spacing. Leveraging the observations of SWPM ultimately provides greater confidence in field development strategy and economic optimization.
Abstract The purpose of this paper is to present a technique to estimate hydraulic fracture (HF) length, fracture conductivity, and fracture efficiency using simple and rapid but rigorous reservoir simulation matching of historical production, and where available, pressure. The methodology is particularly appropriate for analysis of horizontal wells with multiple fractures in tight unconventional or unconventional resource plays. In our discussion, we also analyze the differences between the results from decline curve analysis (DCA) approach and the Science Based Forecasting (SBF) results that this work proposes. When we characterize fracture properties with SBF, we can do a better job of forecasting than if we randomly combine fracture properties and reservoir permeability together in a decline-curve trend. The forecasts are significantly different with SBF, therefore fracture characterization plays an important role and SBF uses this characterization to produce different (and better) forecasts.
Abstract The objective of this study was to perform an integrated analysis to gain insight for optimizing fracturing treatment and gas recovery from Marcellus shale. The analysis involved all the available data from a Marcellus Shale horizontal well which included vertical and lateral well logs, hydraulic fracture treatment design, microseismic, production logging, and production data. A commercial fracturing software was utilized to predict the hydraulic fracture properties based on the available vertical and lateral well logs data, diagnostic fracture injection test (DFIT), fracture stimulation treatment data, and microseismic recordings during the fracturing treatment. The predicted hydraulic fracture properties were then used in a reservoir simulation model developed based on the Marcellus Shale properties to predict the production performance. In this study, the rock mechanical properties were estimated from the well log data. The minimum horizontal stress, instantaneous shut-in pressure (ISIP), process zone stress (PZS), and leak-off mechanism were determined from DFIT analysis. The stress conditions were then adjusted based on the results of microseismic interpretations. Subsequently, the results of the analyses were used in the fracturing software to predict the hydraulic fracture properties. Marcellus Shale properties and the predicted hydraulic fracture properties were used to develop a reservoir simulation model. Porosity, permeability, and the adsorption characteristics were estimated from the core plugs measurements and the well log data. The image logs were utilized to estimate the distribution of natural fractures (fissures). The relation between the formation permeability and the fracture conductivity and the net stress (geomechanical factors) were obtained from the core plugs measurements and published data. The predicted production performance was then compared against production history. The analysis of core data, image logs, and DFIT confirmed the presence of natural fractures in the reservoir. The formation properties and in-situ stress conditions were found to influence the hydraulic fracturing geometry. The hydraulic fracture properties are also impacted by stress shadowing and the net stress changes. The production logging tool results could not be directly related to the hydraulic fracture properties or natural fracture distribution. The inclusion of the stress shadowing, microseismic interpretations, and geomechanical factors provided a close agreement between the predicted production performance and the actual production performance of the well under study.
Agrawal, Shivam (The University of Texas at Austin (now with Sensia Global, Houston)) | York, Jason (The University of Texas at Austin) | Foster, John T. (The University of Texas at Austin) | Sharma, Mukul M. (The University of Texas at Austin)
Summary Hydraulic fracture (HF) modeling is a multiscale and multiphysics problem. It should capture various effects, including those of in-situ stresses, poroelasticity, and reservoir heterogeneities at different length scales. A peridynamics (PD)-based hydraulic fracturing simulator has been demonstrated to reproduce this physics accurately. However, accounting for such details leads to a reduction in computational speed. In this paper, we present a novel coupling of the PD-based simulator with numerically efficient finite element methods (FEMs) and finite volume methods (FVMs) to achieve a significant improvement in computational performance. Unlike classical methods, such as FEM and FVM that solve differential equations, PD uses an integral formulation to circumvent the undefined spatial derivatives at crack tips. We implemented four novel coupling schemes of our PD-based simulator with FEM and FVM: static PD region scheme, dynamic PD region scheme, adaptive mesh refinement scheme, and dynamic mesh coarsening scheme. PD equations are solved using a refined mesh close to the fracture, whereas FE/FV equations are solved using a progressively coarser mesh away from the fracture. As the fracture grows, a dynamic conversion of FE/FV cells to PD nodes and adaptive mesh refinement are incorporated. To improve the performance further, the dynamic mesh coarsening scheme additionally converts the fine PD nodes back to coarse FE/FV cells as the HF grows in length. The coupling schemes are verified against the Kristianovich-Geertsma-de Klerk (KGD) fracture propagation problem. No spurious behavior is observed near the transition between PD and FE/FV regions. In the first three coupling schemes, the computational runtime for single fracture propagation is reduced by up to 10, 20, and 50 times, respectively, compared to a pure PD model. Laboratory experiments on the interaction of an HF with a natural fracture (NF) are revisited. The model captures complex fracture behavior, such as turning in the case of low stress contrast and low angle of interaction, kinking for higher stress contrast or higher angle of interaction, and fracture crossing for near-orthogonal NFs. Moreover, several previously reported phenomena, including fracture propagation at an angle to the principal stress directions, competing fracture growth from multiple closely spaced clusters, and interaction with layers of varying mechanical properties are successfully modeled. Thus, the coupling of PD with FEM and FVM offers an innovative and fundamentally comprehensive solution to alleviate the high computational costs typically associated with the pure PD-based hydraulic fracturing simulations. At the same time, these coupling schemes retain the versatility of the nonlocal PD formulation at modeling the evolution of arbitrary material damage, commonly observed during HF propagation in complex heterogeneous reservoirs.
Pei, Yanli (University of Texas at Austin (Corresponding author) | Yu, Wei (email: email@example.com)) | Sepehrnoori, Kamy (University of Texas at Austin and Sim Tech LLC) | Gong, Yiwen (University of Texas at Austin) | Xie, Hongbing (Sim Tech LLC and Ohio State University) | Wu, Kan (Sim Tech LLC)
Summary The extensive depletion of the development target triggers the demand for infill drilling in the upside target of multilayer unconventional reservoirs. However, such an infill scheme in the field practice still heavily relies on empirical knowledge or pressure responses, and the geomechanics consequences have not been fully understood. Backed by the data set from the Permian Basin, in this work we present a novel integrated reservoir-geomechanics-fracture model to simulate the spatiotemporal stress evolution and locate the optimal development strategy in the upside target of the Bone Spring Formation. An embedded discrete fracture model (EDFM) is deployed in our fluid-flow simulation to characterize complex fractures, and the stress-dependent matrix permeability and fracture conductivity are included through the compaction/dilation option. After calibrating reservoir and fracture properties by history matching of an actual well in the development target (i.e., third Bone Spring), we run the finite element method (FEM)-based geomechanics simulation to model the 3D stress state evolution. Then a displacement discontinuity method (DDM) hydraulic fracture model is applied to simulate the multicluster fracture propagation under an updated heterogeneous stress field in the upside target (i.e., second Bone Spring). Numerical results indicate that stress field redistribution associated with parent-well production indeed vertically propagates to the upside target. The extent of stress reorientation at the infill location mainly depends on the parent-child horizontal offset, whereas the stress depletion is under the combined impact of horizontal offset, vertical offset, and infill time. A smaller parent-child horizontal offset aggravates the overlap of the stimulated reservoir volume (SRV), resulting in more substantial interwell interference and less desirable oil and gas production. The same trend is observed by varying the parent-child vertical offset. Moreover, the efficacy of an infill operation at an earlier time is less affected by parent-well depletion because of the less-disturbed stress state. The candidate infill-well locations at various infill timings are suggested based on the parent-well and child-well production cosimulation. Being able to incorporate both pressure and stress responses, the reservoir-geomechanics-fracture model delivers a more comprehensive understanding and a more integral solution of infill-well design in multilayer unconventional reservoirs. The conclusions provide practical guidelines for the subsequent development in the Permian Basin.
Shi, Xuewen (PetroChina Southwest Oil & Gas Field Company) | Tong, Yanming (Schlumberger China) | Liu, Wenping (PetroChina Southwest Oil & Gas Field Company) | Zhao, Chunduan (Schlumberger China) | Liu, Jia (PetroChina Southwest Oil & Gas Field Company) | Fang, Jian (CCDC Geological E&D Research Institute)
Abstract Ascertaining the characteristics of fracture system at different scales integratedly is very important for performing efficient exploration and development activities of specific shale gas reservoirs. In this paper, an area around 250 square kilometers in Changning Block of Sichuan Basin is taken as an example, which belongs to Chinese Shale Gas Development Demonstration Plot. Seismic structural interpretation was performed detailedly based on original seismic amplitude cube and derived edge-detection cubes, and then the technologies of finite element horizon flattening, orthogonal decomposition principal component analysis, seismic discontinuity patch auto-extraction and paleo-stress field inversion were applied, together with the existing regional geological understanding and fracture information in wells, to figure out the staging and grouping of fracture system at seismic scale (i.e., at large and middle scales), at the same time to clarify the regional tectonic evolution and its genetic relationship with fractures at different scales such as the ones revealed by seismic data and cores or image logs. The following conclusions were reached. (a) The tectonic movements affecting the development of fracture system in study interval mainly happened during Yanshanian-Himalayan periods, i.e., 3 compressional tectonic episodes which were nearly in S-N direction in Late Yanshanian period, in NNE-SSW direction in Early Himalayan period, and in NWW-SEE direction in Middle Himalayan period respectively. (b) The Late Yanshanian tectonic event primarily formed long-axis anticlines and synclines, thrust faults and fault-related fractures, all of which were nearly in E-W trending, and fold-related fractures in different directions. (c) The Early Himalayan tectonic event mainly formed genetically related conjugate fracture sets including strike-slip faults and shear fractures both in NNW and NE directions, and transverse extensional fractures. (d) The Middle Himalayan tectonic event chiefly formed thrust faults, and related fractures and folds in NNE~NE direction, and transverse extensional fractures. (e) Furtherly our work demonstrated that such kind of fracture system analysis was of great significance in building discrete fracture network, providing precautionary advice for drilling engineering, and optimizing completion program and field development plan, etc. Hence, integrated fracture system analysis at full scales to reach more meaningful and robust conclusions is essential work for unconventional resources evaluation and characterization.
Chen, Chi (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University) | Wang, Shouxin (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University) | Lu, Cong (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University) | Wang, Kun (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University) | Lai, Jie (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University) | Liu, Yuxuan (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University)
Abstract Hydraulic fracturing technology provides a guarantee for effective production increase and economic exploitation of shale gas wells reservoirs. Propped fractures formed in the formation after fracturing are the key channels for shale gas production. Accurate evaluation of local propped fracture conductivity is of great significance to the effective development of shale gas. Due to the complex lithology and well-developed bedding of shale, the fracture surface morphology after fracturing is rougher than that of sandstone. This roughness will affect the placement of the proppant in the fracture and thus affect the conductivity. At present, fracture conductivity tests in laboratories are generally based on the standard/modified API/ISO method, ignoring the influence of fracture surface roughness. The inability to obtain the rock samples with the same rough morphology to carry out conductivity testing has always been a predicament in the experimental study on propped fracture conductivity. Herein, we propose a new method to reproduce the original fracture surface, and conductivity test samples with uniform surface morphology, consistent mechanical properties were produced. Then, we have carried out experimental research on shale-propped fracture conductivity. The results show that the fracture surfaces produced by the new method are basically the same as the original fracture surfaces, which fully meet the requirements of the conductivity test. The propped fracture conductivity is affected by proppant properties and fracture surface, especially at low proppant concentration. And increasing proppant concentration will help increase the predictability of conductivity. Due to the influence of the roughness of the fracture surface, there may be an optimal proppant concentration under a certain closure pressure.