Intelligent Fracture Diagnostic Procedure Using Smart Microchip Proppants Data

2021 ◽  
Author(s):  
Vuong Van Pham ◽  
Amirmasoud Kalantari Dahaghi ◽  
Shahin Negahban ◽  
William Fincham ◽  
Aydin Babakhani
Keyword(s):  
Affine Transformation ◽  
Oil And Gas ◽  
Fracture Network ◽  
Unsupervised Clustering ◽  
Sensor Data ◽  
Hydraulic Fractures ◽  
Fracture Geometry ◽  
Novel Approach ◽  
House Design ◽  
Fracture Mapping

Abstract Unconventional oil and gas reservoir development requires an understanding of the geometry and complexity of hydraulic fractures. The current categories of fracture diagnostic approaches include methods for near-wellbore (production and temperature logs, tracers, borehole imaging) and far-field techniques (micro-seismic fracture mapping). These techniques provide an indirect and/or interpreted fracture geometry. Therefore, none of these methods consistently provides a fully detailed and accurate description of the character of created hydraulic fractures. This study proposes a novel approach that uses direct data from the injected fine size and battery-less Smart MicroChip Proppants (SMPs) to map the fracture geometry. This novel approach enables direct, fast, and smart of the received high-resolution geo-sensor data from the SMPs collected in high pressure and high-temperature environment and maps the fracture network using the proposed Intelligent and Integrated Fracture Diagnostic Platform (IFDP), which is a closed-loop architecture and is based on multi-dimensional projection, unsupervised clustering, and surface reconstruction. Affine transformation and a shallow ANN are integrated to control the stochasticity of clustering. IFDP proves its efficacy in fracture diagnostics for 3 in-house design synthetic fracture networks, with 100% consistency, rated "fairly satisfied" to "highly satisfied" in prediction capability, and between 85-100% in execution robustness. The integration of the couple affine transformation-ANN increases the performance of unsupervised clustering in IFDP.

I-GeoSensing Fracture Diagnostic I-GSFD for Fast Processing of the Smart Microchip Proppants Data

2021 ◽  
Author(s):  
Vuong Pham ◽  
Amirmasoud Dahaghi ◽  
Shahin Negahban ◽  
William Fincham ◽  
Aydin Babakhani
Keyword(s):  
Oil And Gas ◽  
Mesh Size ◽  
Fracture Network ◽  
Sensor Data ◽  
Hydraulic Fractures ◽  
Fracture Geometry ◽  
Real Time Processing ◽  
Novel Approach ◽  
Fast Processing ◽  
Fracture Mapping

Abstract Unconventional oil and gas reservoir development requires an understanding of the geometry and complexity of the hydraulic fractures. The current categories of fracture diagnostic approaches include methods for near-wellbore (production & temperature logs, tracers, and borehole imaging) and far-field techniques (micro-seismic fracture mapping). These techniques provide an indirect and interpreted fracture geometry. Therefore, none of these methods consistently provides a fully detailed and accurate description of the characteristic of the subsurface hydraulic fractures. This study proposes a novel approach that uses direct data from the injected fine size (proppant Mesh size equivalence) and battery-less, Smart MicroChip Proppants (SMPs), to map the fracture geometry. This novel approach enables direct, fast, smart, and real-time processing of the received high-resolution geo-sensor data from the SMPs collected in high pressure and high-temperature environment and maps the fracture network using the proposed i-Geosensing Fracture Diagnostic (i-GSFD), which is a closed-loop architecture. i-GSFD is based on multi-dimensional projection unsupervised clustering, and integrated Bayesian optimizer to control the stochastic nature of any components in the loop.

Novel Near-Wellbore Fracture Diagnosis for Unconventional Wells Using High-Resolution Distributed Strain Sensing during Production

SPE Journal ◽  
2021 ◽  
pp. 1-10
Author(s):  
Ge Jin ◽  
Gustavo Ugueto ◽  
Magdalena Wojtaszek ◽  
Artur Guzik ◽  
Dana Jurick ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Fiber Optic ◽  
Fracture Network ◽  
Production Performance ◽  
Hydraulic Fractures ◽  
Fracture Aperture ◽  
Strain Sensing ◽  
Extensional Strain ◽  
Cluster Efficiency ◽  
Distributed Strain

Summary The characteristics of hydraulic fractures in the near-wellbore region contain critical information related to the production performance of unconventional wells. We demonstrate a novel application of a fiber-optic-based distributed strain sensing (DSS) technology to measure and characterize near-wellbore fractures and perforation cluster efficiency during production. Distributed fiber-optic-based strain measurements are made based on the frequency shift of the Rayleigh scatter spectrum, which is linearly dependent on strain and temperature changes of the sensing fiber. Strain changes along the wellbore are continuously measured during the shut-in and reopening operations of a well. After removing temperature effects, extensional strain changes can be observed at locations around the perforation cluster during a shut-in period. We interpret that the observed strain changes are caused by near-wellbore fracture aperture changes caused by pressure increases within the near-wellbore fracture network. The depth locations of the measured strain changes correlate well with distributed acoustic sensing (DAS) acoustic intensity measurements that were measured during the stimulation of the well. The shape and magnitude of the strain changes differ significantly between two completion designs in the same well. Different dependencies between strain and borehole pressure can be observed at most of the perforation clusters between the shut-in and reopening periods. We assess that this new type of distributed fiber-optic measurement method can significantly improve understanding of near-wellbore hydraulic fracture characteristics and the relationships between stimulation and production from unconventional oil and gas wells.

scholarly journals A multi-perforation staged fracturing experimental study on hydraulic fracture initiation and propagation

2020 ◽  
Vol 38 (6) ◽  
pp. 2466-2484
Author(s):  
Jianguang Wei ◽  
Saipeng Huang ◽  
Guangwei Hao ◽  
Jiangtao Li ◽  
Xiaofeng Zhou ◽  
...  
Keyword(s):  
Hydraulic Fracturing ◽  
Hydraulic Fracture ◽  
Oil And Gas ◽  
Fracture Initiation ◽  
Fracture Network ◽  
Hydraulic Fractures ◽  
Breakdown Pressure ◽  
Tight Sandstone ◽  
Heterogeneous Samples ◽  
Initiation And Propagation

Hydraulic fracture initiation and propagation are extremely important on deciding the production capacity and are crucial for oil and gas exploration and development. Based on a self-designed system, multi-perforation cluster-staged fracturing in thick tight sandstone reservoir was simulated in the laboratory. Moreover, the technology of staged fracturing during casing completion was achieved by using a preformed perforated wellbore. Three hydraulic fracturing methods, including single-perforation cluster fracturing, multi-perforation cluster conventional fracturing and multi-perforation cluster staged fracturing, were applied and studied, respectively. The results clearly indicate that the hydraulic fractures resulting from single-perforation cluster fracturing are relatively simple, which is difficult to form fracture network. In contrast, multi-perforation cluster-staged fracturing has more probability to produce complex fractures including major fracture and its branched fractures, especially in heterogeneous samples. Furthermore, the propagation direction of hydraulic fractures tends to change in heterogeneous samples, which is more likely to form a multi-directional hydraulic fracture network. The fracture area is greatly increased when the perforation cluster density increases in multi-perforation cluster conventional fracturing and multi-perforation cluster-staged fracturing. Moreover, higher perforation cluster densities and larger stage numbers are beneficial to hydraulic fracture initiation. The breakdown pressure in homogeneous samples is much higher than that in heterogeneous samples during hydraulic fracturing. In addition, the time of first fracture initiation has the trend that the shorter the initiation time is, the higher the breakdown pressure is. The results of this study provide meaningful suggestions for enhancing the production mechanism of multi-perforation cluster staged fracturing.

A Novel Approach to Reservoir Simulation of Hydraulic Fractures: Performance Improvement Using Pseudo Well Connections

2022 ◽  
Author(s):  
Aamir Lokhandwala ◽  
Vaibhav Joshi ◽  
Ankit Dutt
Keyword(s):  
Reservoir Simulation ◽  
Hydraulic Fracture ◽  
Oil And Gas ◽  
Time Frame ◽  
Simulation Software ◽  
Hydraulic Fractures ◽  
Current Case ◽  
Future Production ◽  
Novel Approach ◽  
Simulation Problem

Abstract Reservoir simulation is used in most modern reservoir studies to predict future production of oil and gas, and to plan the development of the reservoir. The number of hydraulically fractured wells has risen drastically in recent years due to the increase in production in unconventional reservoirs. Gone are the days of using simple analytic techniques to forecast the production of a hydraulic fracture in a vertical well, and the need to be able to model multiple hydraulic fractures in many stages over long horizontals is now a common practice. The type of simulation approach chosen depends on many factors and is study specific. Pseudo well connection approach was preferred in the current case. Due to the nature of the reservoir simulation problem, a decision needs to be made to determine which hydraulic fracture modeling method might be most suitable for any given study. To do this, a selection of methods is chosen based on what is available at hand, and what is commonly used in various reservoir simulation software packages. The pseudo well connection method, which models hydraulic fractures as uniform conductivity rectangular fractures was utilized for a field of interest referred to as Field A in this paper. Such an assumption of the nature of the hydraulic fracture is common in most modern tools. Field A is a low permeability (0.01md-0.1md), tight (8% to 12% porosity) gas-condensate (API ~51deg and CGR~65 stb/mmscf) reservoir at ~3000m depth. Being structurally complex, it has a large number of erosional features and pinch-outs. The pseudo well connection approach was found to be efficient both terms of replicating data of Field A for a 10 year period while drastically reducing simulation runtime for the subsequent 10 year-period too. It helped the subsurface team to test multiple scenarios in a limited time-frame leading to improved project management.

Experimental Study on Injection Pressure Response and Fracture Geometry during Temporary Plugging and Diverting Fracturing

SPE Journal ◽  
2020 ◽  
Vol 25 (02) ◽  
pp. 573-586 ◽  
Author(s):  
Bo Wang ◽  
Fujian Zhou ◽  
Chen Yang ◽  
Daobing Wang ◽  
Kai Yang ◽  
...  
Keyword(s):  
Injection Pressure ◽  
Fracture Network ◽  
Pressure Response ◽  
Hydraulic Fractures ◽  
Fracturing Fluid ◽  
Breakdown Pressure ◽  
Multiple Fractures ◽  
Differential Stress ◽  
Fracture Geometry ◽  
Stimulated Reservoir Volume

Summary Temporary plugging and diverting fracturing (TPDF) has become one of the fastest-growing techniques to maximize the stimulated reservoir volume (SRV). During the field operation of TPDF, diverters are injected to redirect the hydraulic fractures into the under-stimulated region of the reservoir, and, thus, to obtain better coverage of the created fracture network. In this study, the commonly used true tri-axial hydraulic fracturing system is modified to investigate the influences of various factors on the injection pressure response and resultant fracture geometry during diversion treatments. The experimental results show the feasibility of creating multiple fractures through TPDF, and more importantly give the following findings: (1) a complex diverted fracture network tends to be created at a small differential stress (2.5 MPa in this case), while diverted fractures tend to grow parallel to the initial fractures at a high differential stress (7.5 MPa in this case); (2) with the same concentration in the fracturing fluid, 40-mesh powder-shaped diverters can plug the created fractures and increase the net pressure more rapidly than 6-mm fiber-shaped diverters; (3) an excess of diverters can lead to a strong injection pressure response, and, thus, enhance the difficulty of creating multiple fractures; (4) when diverters are injected with the fracturing fluid, no obvious breakdown pressure or propagation pressure is shown during the fracture propagation.

scholarly journals Review of pseudo-three-dimensional modeling approaches in hydraulic fracturing

2021 ◽  
Author(s):  
Hai T. Nguyen ◽  
Jang Hyun Lee ◽  
Khaled A. Elraies
Keyword(s):  
Hydraulic Fracture ◽  
Oil And Gas ◽  
Three Dimensional ◽  
Network Models ◽  
Fracture Network ◽  
Injection Rate ◽  
Height Growth ◽  
Early Developmental Stage ◽  
Hydraulic Fractures ◽  
Equilibrium Height

AbstractIn the field of hydraulic fracture modeling, the pseudo-three-dimensional (P3D) approach is an efficient and practical computational tool serving as a compromise between two-dimensional and planar three-dimensional models. This review discusses the P3D modeling approach from its early developmental stage in the 1980s to the present. The evolution of P3D modeling is drawn over time based on the major differences in the governing formulation and assumptions considered by each model. The problems of equilibrium height growth and vertical viscous fluid resistance (i.e., non-equilibrium height growth) emphasize the primary differences among these models. Besides, the P3D-based complex fracture network models for shale oil and gas reservoirs accounting for the interaction between preexisting natural fractures and induced hydraulic fractures are discussed. Finally, in the application section, several simulations are reported to demonstrate the validation of the P3D numerical algorithm by comparing it with the Perkins–Kern–Nordgren (PKN) large and small asymptotic solutions, as well as the effect of time-dependent variable injection rates on the hydraulic fracture propagation. The results showed a good matching between P3D and PKN solutions and a significant effect of the wellbore variable injection rate on the evolution of the fracture length.

The relation between stimulated shear fractures and production in the Barnett Shale: Implications for unconventional oil and gas reservoirs

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. B461-B469 ◽  
Author(s):  
Alex Hakso ◽  
Mark Zoback
Keyword(s):  
Hydraulic Fracturing ◽  
Oil And Gas ◽  
Shear Fracture ◽  
Fracture Network ◽  
Early Years ◽  
Barnett Shale ◽  
Hydraulic Fractures ◽  
Low Permeability ◽  
Distributed Temperature Sensing ◽  
Fracture Planes

Economic production from extremely low permeability unconventional reservoirs is accomplished through multistage slick water hydraulic fracturing, which generates opening-mode hydraulic fractures and induces shear slip on preexisting fractures in the surrounding formation. We have addressed the critical contribution of the stimulated shear fracture network on production. We found production decline curves from tens of thousands of wells in four unconventional plays in the U.S. (two oil and two gas). These data indicate that during the early years of production: (1) Production is dominated by linear flow from the extremely low permeability matrix into much more permeable fracture planes, (2) the rapid decrease in production rates is a natural consequence of pressure depletion in the matrix within several meters of the more permeable planes, and (3) the cumulative area of permeable fracture planes created during stimulation is an important factor affecting cumulative production. Using data from two case studies in the Barnett Shale, we estimate the area of the fracture network from the microseismicity generated during hydraulic fracturing operations. The data from one study demonstrates that the cumulative area of the shear fracture network is needed to match production data. With data from the other case study, we demonstrate that the relative fracture area created during each stage correlates well with the relative stage-by-stage production determined from distributed temperature sensing.

Time-lapse synchrotron X-ray imaging of deformation modes in organic-rich Green River Shale heated under confinement 

2021 ◽  
Author(s):  
Maya Kobchenko ◽  
Anne Pluymakers ◽  
Benoit Cordonnier ◽  
Nazmul Mondol ◽  
Francois Renard
Keyword(s):  
Organic Matter ◽  
Oil And Gas ◽  
Fracture Network ◽  
High Energy ◽  
External Loading ◽  
Hydraulic Fractures ◽  
Burial History ◽  
X Ray ◽  
In Situ Conditions

<p>Shales are layered sedimentary rocks, which can be almost impermeable for fluids and act as seals and cap-rocks, or if a shale layer hosts a fracture network, it can act as a fluid reservoir and/or conduit. Organic-rich shales contain organic matter - kerogen, which can transform from solid-state to oil and gas during burial and exposure to a suitable temperature. When hydrocarbons are expelled from the organic matter due to maturation, pore-pressure increases, which drives the propagation of hydraulic fractures, a mechanism identified to explain oil and gas primary migration. Density, geometry, extension, and connectivity of the final fracture network depend on the combination of the heating conditions and history of external loading experienced by the shale. Here, we have performed a series of rock physics experiments where organic-rich shale samples were heated, under in situ conditions, and the development of microfractures was imaged through time. We used the high-energy X-ray beam produced at the European Synchrotron Radiation Facility to acquire dynamic microtomography images and monitor different modes of shale deformation in-situ in 3D. We reproduced natural conditions of the shale deformation processes using a combination of axial load, confining pressure, and heating of the shale samples. Shales feature natural sedimentary laminations and hydraulic fractures propagate parallel to these laminae if no overburden stress is applied. However, if the principal external load becomes vertical, perpendicular to the shale lamination, the fracture propagation direction can deviate from the horizontal one. Together horizontal and vertical fractures form a three-dimensional connected fracture network, which provides escaping pathways for generated hydrocarbons. Our experiments demonstrate that tight shale rocks, which are often considered impermeable, could host transient episodes of micro-fracturing and high permeability during burial history.</p>

scholarly journals Machine Learning Solutions for Bridge Scour Forecast Based on Monitoring Data

2021 ◽  
pp. 036119812110126
Author(s):  
Negin Yousefpour ◽  
Steve Downie ◽  
Steve Walker ◽  
Nathan Perkins ◽  
Hristo Dikanski
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Monitoring Data ◽  
Sensor Data ◽  
Monitoring Systems ◽  
Bridge Scour ◽  
Scour Monitoring ◽  
Novel Approach ◽  
Bayesian Inference Method ◽  
Water Level Variations

Bridge scour is a challenge throughout the U.S.A. and other countries. Despite the scale of the issue, there is still a substantial lack of robust methods for scour prediction to support reliable, risk-based management and decision making. Throughout the past decade, the use of real-time scour monitoring systems has gained increasing interest among state departments of transportation across the U.S.A. This paper introduces three distinct methodologies for scour prediction using advanced artificial intelligence (AI)/machine learning (ML) techniques based on real-time scour monitoring data. Scour monitoring data included the riverbed and river stage elevation time series at bridge piers gathered from various sources. Deep learning algorithms showed promising in prediction of bed elevation and water level variations as early as a week in advance. Ensemble neural networks proved successful in the predicting the maximum upcoming scour depth, using the observed sensor data at the onset of a scour episode, and based on bridge pier, flow and riverbed characteristics. In addition, two of the common empirical scour models were calibrated based on the observed sensor data using the Bayesian inference method, showing significant improvement in prediction accuracy. Overall, this paper introduces a novel approach for scour risk management by integrating emerging AI/ML algorithms with real-time monitoring systems for early scour forecast.

scholarly journals A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 757
Author(s):  
Yongke Pan ◽  
Kewen Xia ◽  
Li Wang ◽  
Ziping He
Keyword(s):  
Optimization Algorithm ◽  
Oil And Gas ◽  
The Other ◽  
Whale Optimization Algorithm ◽  
Classification Model ◽  
Support Vector ◽  
Recognition Model ◽  
Novel Approach ◽  
Whale Optimization ◽  
Oil And Gas Reservoir

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

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