Technology Focus: Hydraulic Fracturing Modeling (November 2021)

2021 ◽  
Vol 73 (11) ◽  
pp. 64-64
Author(s):  
Junjie Yangfi
Keyword(s):  
Deep Learning ◽  
Fractured Reservoirs ◽  
Operating Conditions ◽  
Data Availability ◽  
Natural Fracture ◽  
Hydraulic Fractures ◽  
Fine Grid ◽  
Modeling Process ◽  
Fracture Systems ◽  
Fracture Modeling

In the past decades, the success of unconventional hydrocarbon resource development can be attributed primarily to the improved understanding of fracture systems, including both hydraulically induced fractures and natural fracture networks. To tackle the fracture characterization problem, several recent papers have provided novel insights into fracture modeling technique. Because of the complex nature and heterogeneity of rock discontinuity, fabric, and texture, the fracture-modeling process typically suffers from limited data availability. Research shows that modeling results reached without interrogation of high-resolution petrophysical and geomechanical data can mislead because the fluid flow is actually controlled by fine-scale rock properties. A more-reliable fracture geometry can be obtained from an enhanced modeling process that preserves the signature from high-frequency data. Advanced techniques to model fracturing processes with proppant transportation and thermodynamics require even more-sophisticated simulation and computation power. When the subsurface is too puzzling to be described by a physical model and existing data, machine learning and artificial intelligence can be adapted as a practical alternative to interpret complex fracture systems. Taking a discrete fracture network (DFN) as an example, a data-driven approach has been introduced to learn from outcrop, borehole imaging, core computed tomography scan, and seismic data to recognize stratigraphic bedding, faults, subseismic fractures, and hydraulic fractures. Input data can be collected by hand, 3D stereophotogrammetry, or drone. When upscaling DFN into a coarse grid for reservoir simulation, deep-learning techniques such as convolutional neuron networks can be used to populate fracture properties into a dual-porosity/dual-permeability model approved to yield high accuracy compared with a fine-grid model. Furthermore, the new techniques greatly extend the application of fracture modeling in the arena of the energy transition, such as in geothermal optimization. Recommended additional reading at OnePetro: www.onepetro.org. SPE 203927 - Numerical Simulation of Proppant Transport in Hydraulically Fractured Reservoirs by Seyhan Emre Gorucu, Computer Modelling Group, et al. SPE 202679 - Deep-Learning Approach To Predict Rheological Behavior of Supercritical CO2 Foam Fracturing Fluid Under Different Operating Conditions by Shehzad Ahmed, Khalifa University of Science and Technology, et al. SPE 203983 - A 3D Coupled Thermal/Hydraulic/Mechanical Model Using EDFM and XFEM for Hydraulic-Fracture-Dominated Geothermal Reservoirs by Xiangyu Yu, Colorado School of Mines, et al.

Numerical modeling of seismicity induced by fluid injection in naturally fractured reservoirs

Geophysics ◽  
2011 ◽  
Vol 76 (6) ◽  
pp. WC167-WC180 ◽  
Author(s):  
Xueping Zhao ◽  
R. Paul Young
Keyword(s):  
Numerical Modeling ◽  
Fractured Reservoirs ◽  
Seismic Source ◽  
Microseismic Monitoring ◽  
Naturally Fractured Reservoirs ◽  
Natural Fracture ◽  
Hydraulic Fractures ◽  
Field Observations ◽  
Natural Fractures ◽  
Naturally Fractured

The interaction between hydraulic and natural fractures is of great interest for the energy resource industry because natural fractures can significantly influence the overall geometry and effectiveness of hydraulic fractures. Microseismic monitoring provides a unique tool to monitor the evolution of fracturing around the treated rock reservoir, and seismic source mechanisms can yield information about the nature of deformation. We performed a numerical modeling study using a 2D distinct-element particle flow code ([Formula: see text]) to simulate realistic conditions and increase understanding of fracturing mechanisms in naturally fractured reservoirs, through comparisons with results of the geometry of hydraulic fractures and seismic source information (locations, magnitudes, and mechanisms) from both laboratory experiments and field observations. A suite of numerical models with fully dynamic and hydromechanical coupling was used to examine the interaction between natural and induced fractures, the effect of orientation of a preexisting fracture, the influence of differential stress, and the relationship between the fluid front, fracture tip, and induced seismicity. The numerical results qualitatively agree with the laboratory and field observations, and suggest possible mechanics for new fracture development and their interaction with a natural fracture (e.g., a tectonic fault). Therefore, the tested model could help in investigating the potential extent of induced fracturing in naturally fractured reservoirs, and in interpreting microseismic monitoring results to assess the effectiveness of a hydraulic fracturing project.

scholarly journals Embedded Discrete Fracture Modeling as a Method to Upscale Permeability for Fractured Reservoirs

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 812 ◽  
Author(s):  
Zhenzhen Dong ◽  
Weirong Li ◽  
Gang Lei ◽  
Huijie Wang ◽  
Cai Wang
Keyword(s):  
Fluid Flow ◽  
Multiphase Flow ◽  
Fractured Reservoirs ◽  
Three Dimensional ◽  
Detailed Comparison ◽  
Fine Grid ◽  
Equivalent Permeability ◽  
Fracture Modeling ◽  
Discrete Fracture ◽  
Novel Method

Fractured reservoirs are distributed widely over the world, and describing fluid flow in fractures is an important and challenging topic in research. Discrete fracture modeling (DFM) and equivalent continuum modeling are two principal methods used to model fluid flow through fractured rocks. In this paper, a novel method, embedded discrete fracture modeling (EDFM), is developed to compute equivalent permeability in fractured reservoirs. This paper begins with an introduction on EDFM. Then, the paper describes an upscaling procedure to calculate equivalent permeability. Following this, the paper carries out a series of simulations to compare the computation cost between DFM and EDFM. In addition, the method is verified by embedded discrete fracture modeling and fine grid methods, and grid-block and multiphase flow are studied to prove the feasibility of the method. Finally, the upscaling procedure is applied to a three-dimensional case in order to study performance for a gas injection problem. This study is the first to use embedded discrete fracture modeling to compute equivalent permeability for fractured reservoirs. This paper also provides a detailed comparison and discussion on embedded discrete fracture modeling and discrete fracture modeling in the context of equivalent permeability computation with a single-phase model. Most importantly, this study addresses whether this novel method can be used in multiphase flow in a reservoir with fractures.

Surrounding Vehicles’ Contribution to Car-Following Models: Deep-Learning-Based Analysis

2021 ◽  
pp. 036119812110186
Author(s):  
Saeed Vasebi ◽  
Yeganeh M. Hayeri ◽  
Peter J. Jin
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Short Term Memory ◽  
Data Availability ◽  
Car Following ◽  
Preceding Vehicle ◽  
Long Short Term Memory ◽  
The Right ◽  
Mean Square Errors ◽  
Deep Learning Model

Relatively recent increased computational power and extensive traffic data availability have provided a unique opportunity to re-investigate drivers’ car-following (CF) behavior. Classic CF models assume drivers’ behavior is only influenced by their preceding vehicle. Recent studies have indicated that considering surrounding vehicles’ information (e.g., multiple preceding vehicles) could affect CF models’ performance. An in-depth investigation of surrounding vehicles’ contribution to CF modeling performance has not been reported in the literature. This study uses a deep-learning model with long short-term memory (LSTM) to investigate to what extent considering surrounding vehicles could improve CF models’ performance. This investigation helps to select the right inputs for traffic flow modeling. Five CF models are compared in this study (i.e., classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models). Performance of the CF models is compared in relation to accuracy, stability, and smoothness of traffic flow. The CF models are trained, validated, and tested by a large publicly available dataset. The average mean square errors (MSEs) for the classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models are 1.58 × 10−3, 1.54 × 10−3, 1.56 × 10−3, 1.61 × 10−3, and 1.73 × 10−3, respectively. However, the results show insignificant performance differences between the classic CF model and multi-anticipative model or adjacent-lanes model in relation to accuracy, stability, or smoothness. The following-vehicle CF model shows similar performance to the multi-anticipative model. The all-surrounding-vehicles CF model has underperformed all the other models.

Numerical Simulation of Complex Fracture Network Development by Hydraulic Fracturing in Naturally Fractured Ultratight Formations

2013 ◽  
Author(s):  
Hannes Hofmann ◽  
Tayfun Babadagli ◽  
Günter Zimmermann
Keyword(s):  
Fracture Network ◽  
Natural Fracture ◽  
Fracture Networks ◽  
Network Development ◽  
Complex Fracture ◽  
Fracture Patterns ◽  
Fracture Systems ◽  
Fracture Development ◽  
Complex Fracture Network ◽  
Treatment Designs

The creation of large complex fracture networks by hydraulic fracturing is imperative for enhanced oil recovery from tight sand or shale reservoirs, tight gas extraction, and Hot-Dry-Rock (HDR) geothermal systems to improve the contact area to the rock matrix. Although conventional fracturing treatments may result in bi-wing fractures, there is evidence by microseismic mapping that fracture networks can develop in many unconventional reservoirs, especially when natural fracture systems are present and the differences between the principle stresses are low. However, not much insight is gained about fracture development as well as fluid and proppant transport in naturally fractured tight formations. In order to clarify the relationship between rock and treatment parameters, and resulting fracture properties, numerical simulations were performed using a commercial Discrete Fracture Network (DFN) simulator. A comprehensive sensitivity analysis is presented to identify typical fracture network patterns resulting from massive water fracturing treatments in different geological conditions. It is shown how the treatment parameters influence the fracture development and what type of fracture patterns may result from different treatment designs. The focus of this study is on complex fracture network development in different natural fracture systems. Additionally, the applicability of the DFN simulator for modeling shale gas stimulation and HDR stimulation is critically discussed. The approach stated above gives an insight into the relationships between rock properties (specifically matrix properties and characteristics of natural fracture systems) and the properties of developed fracture networks. Various simulated scenarios show typical conditions under which different complex fracture patterns can develop and prescribe efficient treatment designs to generate these fracture systems. Hydraulic stimulation is essential for the production of oil, gas, or heat from ultratight formations like shales and basement rocks (mainly granite). If natural fracture systems are present, the fracturing process becomes more complex to simulate. Our simulation results reveal valuable information about main parameters influencing fracture network properties, major factors leading to complex fracture network development, and differences between HDR and shale gas/oil shale stimulations.

An Improved Multicomponent Diffusion Model for Compositional Simulation of Fractured Unconventional Reservoirs

SPE Journal ◽  
2021 ◽  
pp. 1-26
Author(s):  
Ye Tian ◽  
Chi Zhang ◽  
Zhengdong Lei ◽  
Xiaolong Yin ◽  
Hossein Kazemi ◽  
...  
Keyword(s):  
Diffusion Model ◽  
Fractured Reservoirs ◽  
Multicomponent Diffusion ◽  
Unconventional Reservoirs ◽  
Fickian Diffusion ◽  
Minor Effect ◽  
Matrix Interface ◽  
Fine Grid ◽  
Soaking Time ◽  
Fluid Saturation

Summary Most simulators currently use the advection/diffusion model (ADM), where the total flux comprises Darcian advection and Fickian diffusion. However, significant errors can arise, especially for modeling diffusion processes in fractured unconventional reservoirs, if diffusion is modeled by the conventional Fick’s law using molar concentration. Hence, we propose an improved multicomponent diffusion model for fractured reservoirs to better quantify the multiphase multicomponent transport across the fracture/matrix interface. We first give a modified formulation of the Maxwell-Stefan (MS) equation to model the multicomponent diffusion driven by the chemical potential gradients. A physics-based modification is proposed for the ADM in fractured reservoirs, where fracture, matrix, and their interface are represented by three different yet interconnected flow domains to honor the flux continuity at the fracture/matrix interface. The added interface using a more representative fluid saturation and composition of the interface can hence better capture the transient mass fluxes between fracture and matrix. The proposed approach is also implemented in an in-house compositional simulator. The multicomponent diffusion model is validated with both intraphase and interphase diffusion experiments. Then, the improved model for fracture/matrix interaction is compared with a fine-grid model. The proposed multiple interacting continua (MINC) model with three continua (MINC3) can better match the fine-grid model’s result than the double-porosity (DP) model, which only obtains a fair match at an early time. Then, we simulate a gas huff ‘n’ puff (HnP) well in the Permian Basin to investigate the effect of diffusion within the fractured tight oil reservoir. The simulation reveals that diffusion has a minor effect on the performance of depletion when oil is the dominant phase. For gas HnP, the simulation neglecting diffusion will underestimate the oil recovery factor (RF) but overestimate the gas rate. The DP approach tends to overestimate the RF of heavy components but leads to a similar cumulative oil RF compared with MINC3. With the diffusion included in the simulation, gas HnP performance becomes more sensitive to the soaking time than the model without diffusion. Although increasing the soaking time will lead to a higher RF after considering diffusion, the incremental oil is not sufficiently large to justify a prolonged soaking time.

Prognostics and RUL Estimations of SAC305, SAC105 and SnAg Solders Under Temperature and Vibration Using Long Short-Term Memory (LSTM) Deep Learning

2021 ◽  
Author(s):  
Pradeep Lall ◽  
Tony Thomas ◽  
Ken Blecker
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Short Term ◽  
Term Memory ◽  
Rectangular Pattern ◽  
Useful Life ◽  
Long Short Term Memory

Abstract Prognostics and Remaining Useful Life (RUL) estimations of complex systems are essential to operational safety, increased efficiency, and help to schedule maintenance proactively. Modeling the remaining useful life of a system with many complexities is possible with the rapid development in the field of deep learning as a computational technique for failure prediction. Deep learning can adapt to multivariate parameters complex and nonlinear behavior, which is difficult using traditional time-series models for forecasting and prediction purposes. In this paper, a deep learning approach based on Long Short-Term Memory (LSTM) network is used to predict the remaining useful life of the PCB at different conditions of temperature and vibration. This technique can identify the different underlying patterns in the time series that can predict the RUL. This study involves feature vector identification and RUL estimations for SAC305, SAC105, and Tin Lead solder PCBs under different vibration levels and temperature conditions. The acceleration levels of vibration are fixed at 5g and 10g, while the temperature levels are 55°C and 100°C. The test board is a multilayer FR4 configuration with JEDEC standard dimensions consists of twelve packages arranged in a rectangular pattern. Strain signals are acquired from the backside of the PCB at symmetric locations to identify the failure of all the packages during vibration. The strain signals are resistance values that are acquired simultaneously during the experiment until the failure of most of the packages on the board. The feature vectors are identified from statistical analysis on the strain signals frequency and instantaneous frequency components. The principal component analysis is used as a data reduction technique to identify the different patterns produced from the four strain signals with failures of the packages during vibration. LSTM deep learning method is used to model the RUL of the packages at different individual operating conditions of vibration for all three solder materials involved in this study. A combined model for RUL prediction for a material that can take care of the changes in the operating conditions is also modeled for each material.

Pressure-Transient Tests and Flow Regimes in Fractured Reservoirs

2015 ◽  
Vol 18 (02) ◽  
pp. 187-204 ◽  
Author(s):  
Fikri Kuchuk ◽  
Denis Biryukov
Keyword(s):  
History Matching ◽  
Fractured Reservoirs ◽  
Flow Regimes ◽  
Naturally Fractured Reservoirs ◽  
Dual Porosity ◽  
Hydraulic Fractures ◽  
Fractured Reservoir ◽  
Pressure Transient ◽  
Matrix Block ◽  
Naturally Fractured

Summary Fractures are common features in many well-known reservoirs. Naturally fractured reservoirs include fractured igneous, metamorphic, and sedimentary rocks (matrix). Faults in many naturally fractured carbonate reservoirs often have high-permeability zones, and are connected to numerous fractures that have varying conductivities. Furthermore, in many naturally fractured reservoirs, faults and fractures can be discrete (rather than connected-network dual-porosity systems). In this paper, we investigate the pressure-transient behavior of continuously and discretely naturally fractured reservoirs with semianalytical solutions. These fractured reservoirs can contain periodically or arbitrarily distributed finite- and/or infinite-conductivity fractures with different lengths and orientations. Unlike the single-derivative shape of the Warren and Root (1963) model, fractured reservoirs exhibit diverse pressure behaviors as well as more than 10 flow regimes. There are seven important factors that dominate the pressure-transient test as well as flow-regime behaviors of fractured reservoirs: (1) fractures intersect the wellbore parallel to its axis, with a dipping angle of 90° (vertical fractures), including hydraulic fractures; (2) fractures intersect the wellbore with dipping angles from 0° to less than 90°; (3) fractures are in the vicinity of the wellbore; (4) fractures have extremely high or low fracture and fault conductivities; (5) fractures have various sizes and distributions; (6) fractures have high and low matrix block permeabilities; and (7) fractures are damaged (skin zone) as a result of drilling and completion operations and fluids. All flow regimes associated with these factors are shown for a number of continuously and discretely fractured reservoirs with different well and fracture configurations. For a few cases, these flow regimes were compared with those from the field data. We performed history matching of the pressure-transient data generated from our discretely and continuously fractured reservoir models with the Warren and Root (1963) dual-porosity-type models, and it is shown that they yield incorrect reservoir parameters.

scholarly journals Deep learning-based anomaly-onset aware remaining useful life estimation of bearings

2021 ◽  
Vol 7 ◽  
pp. e795
Author(s):  
Pooja Vinayak Kamat ◽  
Rekha Sugandhi ◽  
Satish Kumar
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Short Term Memory ◽  
Rotating Machinery ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Learning Models ◽  
Vibration Data ◽  
Degradation Data ◽  
Useful Life

Remaining Useful Life (RUL) estimation of rotating machinery based on their degradation data is vital for machine supervisors. Deep learning models are effective and popular methods for forecasting when rotating machinery such as bearings may malfunction and ultimately break down. During healthy functioning of the machinery, however, RUL is ill-defined. To address this issue, this study recommends using anomaly monitoring during both RUL estimator training and operation. Essential time-domain data is extracted from the raw bearing vibration data, and deep learning models are used to detect the onset of the anomaly. This further acts as a trigger for data-driven RUL estimation. The study employs an unsupervised clustering approach for anomaly trend analysis and a semi-supervised method for anomaly detection and RUL estimation. The novel combined deep learning-based anomaly-onset aware RUL estimation framework showed enhanced results on the benchmarked PRONOSTIA bearings dataset under non-varying operating conditions. The framework consisting of Autoencoder and Long Short Term Memory variants achieved an accuracy of over 90% in anomaly detection and RUL prediction. In the future, the framework can be deployed under varying operational situations using the transfer learning approach.

Analytical and Machine-LearningAnalysis of Hydraulic Fracture-Induced Natural Fracture Slip

SPE Journal ◽  
2021 ◽  
pp. 1-17
Author(s):  
Peidong Zhao ◽  
K. E. Gray
Keyword(s):  
Failure Modes ◽  
Statistical Significance ◽  
Frictional Coefficient ◽  
Natural Fracture ◽  
Hydraulic Fractures ◽  
Well Performance ◽  
Stimulated Reservoir Volume ◽  
Relative Angle ◽  
Statistical Measures ◽  
Net Pressure

Summary Stimulated reservoir volume (SRV) is a prime factor controlling well performance in unconventional shale plays. In general, SRV describes the extent of connected conductive fracture networks within the formation. Being a pre-existing weak interface, natural fractures (NFs) are the preferred failure paths. Therefore, the interaction of hydraulic fractures (HFs) and NFs is fundamental to fracture growth in a formation. Field observations of induced fracture systems have suggested complex failure zones occurring in the vicinity of HFs, which makes characterizing the SRV a significant challenge. Thus, this work uses a broad range of subsurface conditions to investigate the near-tip processes and to rank their influences on HF-NF interaction. In this study, a 2D analytical workflow is presented that delineates the potential slip zone (PSZ) induced by a HF. The explicit description of failure modes in the near-tip region explains possible mechanisms of fracture complexity observed in the field. The parametric analysis shows varying influences of HF-NF relative angle, stress state, net pressure, frictional coefficient, and HF length to the NF slip. This work analytically proves that an NF at a 30 ± 5° relative angle to an HF has the highest potential to be reactivated, which dominantly depends on the frictional coefficient of the interface. The spatial extension of the PSZ normal to the HF converges as the fracture propagates away and exhibits asymmetry depending on the relative angle. Then a machine-learning (ML) model [random forest (RF) regression] is built to replicate the physics-based model and statistically investigate parametric influences on NF slips. The ML model finds statistical significance of the predicting features in the order of relative angle between HF and NF, fracture gradient, frictional coefficient of the NF, overpressure index, stress differential, formation depth, and net pressure. The ML result is compared with sensitivity analysis and provides a new perspective on HF-NF interaction using statistical measures. The importance of formation depth on HF-NF interaction is stressed in both the physics-based and data-driven models, thus providing insight for field development of stacked resource plays. The proposed concept of PSZ can be used to measure and compare the intensity of HF-NF interactions at various geological settings.

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