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Search Jin, Guodong: Rock mechanical properties
...IPTC-22309-MS Frequency Dependent Rock Mechanical Properties for Geomechanical Applications Shujath Ali Syed and Guodong Jin, Baker Hughes; Shouxiang Mark Ma, ...ncluding wellbore stability evaluation, sanding assessment, and hydraulic fracturing design require rock mechanical properties (e.g. Young's modulus) as inputs. Significant discrepancy exists for the same property measured wit...n the applications. This paper presents the development of a prediction model enabling to determine mechanical properties consistently at any applied frequency. To build the prediction model, we first conducted measuremen...
...onal static vs dynamic correlations for various geomechanically applications. Introduction Static rock mechanical properties are required as input in many applications, including in-situ stress determination, optimum mud win...racturing design, and drilling operation optimization. They are commonly determined from laboratory mechanical testing on subsurface core samples, which are only available in cored wells at discrete depths. ...Rock mechanical properties obtained from such ...
...udy is to determine methods for effectively bridging the gap between the laboratory measured static rock elastic ...properties and that of log derived dynamic elastic ...properties, by considering the effects of frequency and strain amplitude. Additionally, Young's Modulus obtain...
Abstract Geomechanical applications including wellbore stability evaluation, sanding assessment, and hydraulic fracturing design require rock mechanical properties (e.g. Young's modulus) as inputs. Significant discrepancy exists for the same property measured with various techniques due to different loading frequency and deformation amplitude applied, potentially resulting in added uncertainties in the applications. This paper presents the development of a prediction model enabling to determine mechanical properties consistently at any applied frequency. To build the prediction model, we first conducted measurements of Young's modulus and Poisson's ratio on sandstone samples over a wide frequency range from laboratory standard triaxial tests (~10 Hz), downhole logging (~20 KHz), to laboratory ultrasonic measurement (~1 MHz). These data provide a better understanding of frequency-dependent rock mechanical properties. Rock samples having different porosities and permeabilities are selected for investigating their effects on frequency-dependent acoustic wave velocities. Static measurements of Young's modulus and Poisson's ratio are also conducted to complete the measurements spectrum from static to dynamic frequencies. From the experimental data, the prediction model is developed to correlate rock elastic properties with measurement frequencies, which is further used to determine mechanical properties at any desired frequency for various geomechanically applications. As expected, the measured Young's modulus increases as the applied frequency increases, which is mainly due to the stiffening mechanism of the rock. The dispersion analysis of the results indicated a higher degree of stiffening for the higher porosity samples. The prediction model of Young's modulus vs the frequency was built and used to calculate the Young's modulus at the logging frequency from the available ultrasonic measurements. The predicted Young's modulus is compared well with the actual values obtained from acoustic logging data. On the opposite, Young's modulus at the ultrasonic frequency was calculated from the logging data using the prediction model and compared well with the measured Young's modulus at the ultrasonic frequency. Good agreement between the predicted and measured Young's moduli demonstrates the effectiveness of the prediction model, and its capability to derive the desired Young's modulus, such as the static, from the dynamic values measured from downhole logging data. The prediction model was developed from a physics based approach to derive the desired rock mechanical properties from their dynamic values measured at the logging or any other frequency, which potentially makes it unnecessary to develop traditional static vs dynamic correlations for various geomechanically applications.
- Europe > Norway > North Sea > Central North Sea > Central Graben > PL 018 > Block 2/4 > Greater Ekofisk Field > Ekofisk Field > Tor Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Central Graben > PL 018 > Block 2/4 > Greater Ekofisk Field > Ekofisk Field > Ekofisk Formation (0.99)
Effect of Drilling Fluid on Rock Mechanical Properties at Near-Drilling Conditions: An Implication of Fluid Design on Wellbore Stability
Yadav, Prahlad Kumar (Baker Hughes) | Ali, Syed Shujath (Baker Hughes) | Tawat, Najeeb Ahmed Al (Baker Hughes) | Dhamen, Ali Abdullah Al (Baker Hughes) | Jin, Guodong (Baker Hughes)
...OTC-26460-MS Effect of Drilling Fluid on Rock Mechanical Properties at Near-Drilling Conditions: An Implication of Fluid Design on Wellbore Stability Prahlad Kumar Ya...irs can be a wrong choice when it is used for unconventional formations. Drilling fluid has a chemo-mechanical effect on the reservoir ...rock during exposure time; this interaction can be abrupt or imperceptible depending on minerals compris...
...ive drilling mud must be designed which can inhibit reactive shales. A complete characterization of rock samples using various laboratory techniques such as thin-section analysis, scanning electron micros...tion of the shale because of ion movement, and thus, the matrix physical-chemical and geomechanical properties can be altered (Zhang et al., 2006). The adsorption of water results in increased ...rock strength and elastic modulus change (Amanullah et al., 1994, Ewy and Bovberg, 2008). The changes ar...
... 5 Results and Discussion Drilling fluid design Three fluids are formulated with good rheological properties and fluid loss control: 10-ppg customized fluid (CFWBM), HPWBM fluid and OBM. The fluids are prepar...ed using the Silverson L4 RT mixer. Table 3 lists the fluid properties before and after they are hot-rolled for 16 hours. Before geomechanical tests, all samples except f...or MN7HB are contacted with the corresponding drilling fluids for 16 hours at 230 F. Table 3--Properties of drilling fluids before and after hot-rolling at 230 F for 16 hours. ...
Abstract Selecting a drilling fluid from the learnings from conventional reservoirs can be a wrong choice when it is used for unconventional formations. Drilling fluid has a chemo-mechanical effect on the reservoir rock during exposure time; this interaction can be abrupt or imperceptible depending on minerals comprising the rock matrix and their chemical sensitivity to the fluid composition. Improper selection of drilling fluid may cause strong shale-fluid interaction and thus result in wellbore instability. This paper presents a comprehensive experimental study examining the effect of various drilling fluids on the mechanical properties of conventional and unconventional rock samples. Four drilling fluids with varying additives are selected to contact and saturate rock samples at the temperature of 230ยฐF for 16 or 24 hours: Three of them are water-based muds (WBM) and the other one is an oil-based mud (OBM). Rock samples used are from the Berea sandstone, Mancos and Eagle Ford shale formations. For each type of rock, one plug is tested without contacting any drilling fluid and is used as a reference of geomechanical properties. Other samples are contacted and saturated with other drilling fluids before their geomechanical testing. A fluid-saturating process is conducted at a pressurized aging cell. Mechanical testing is performed in a servo-controlled triaxial apparatus in which samples are deformed at a constant confining pressure of 10 MPa and the drained condition. Experimental results show that drilling fluids have a negligible effect on the peak strength and Young's moduli of Berea sandstone. However, the peak strength of Mancos shales decreases dramatically while their Young's moduli change randomly. For Eagle Ford shales, fluids reduce both peak strength and Young's moduli. For all samples tested, their Poisson's ratios increase after samples are saturated with fluids. Compared to WBM, it is observed that OBM preserves the mechanical properties of Mancos shales much better. After optimizing the design of one high-performance water-based mud (HPWBM1), the new fluid (HPWBM2) has an improved performance (similar to OBM) in preserving shale geomechanical properties.
- North America > United States > Texas (1.00)
- North America > United States > Colorado (0.71)
- Research Report > New Finding (0.69)
- Research Report > Experimental Study (0.55)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Eagle Ford Shale Formation (0.98)
- North America > United States > Texas > Sabinas - Rio Grande Basin > Eagle Ford Shale Formation (0.98)
- North America > United States > Texas > Maverick Basin > Eagle Ford Shale Formation (0.98)
- (6 more...)
Linking Geochemical and Mechanical Properties of Rock Samples Using New Non-Destructive Techniques
Hussain, Maaruf (Baker Hughes, a GE Company) | Amao, AbdulJamiu (Geosciences Department, College of Petroleum Engineering and Geosciences, KFUPM) | Jin, Guodong (Baker Hughes, a GE Company) | Al-Ramadan, Khalid (Geosciences Department, College of Petroleum Engineering and Geosciences, KFUPM)
...SPE-192347-MS Linking Geochemical and Mechanical Properties of ...Rock Samples Using New Non-Destructive Techniques Maaruf Hussain, Baker Hughes, a GE Company; AbdulJami...asingly being used to determined and quantify the abundances of the major, trace elements and other rock properties. This study utilized a combination of dispersive spectrometric techniques (MicroXRF) and impulse re...
...ormed using triaxial tests data output, such as the uniaxial compressive strength (UCS) and elastic properties of rocks (Young's modulus, Poisson's ratio, etc.), that are empirically linked to wireline data. Sa...mple availability, representativeness, time, and cost are problems associated with core-based rock measurements for ...mechanical properties [3; 4]. There is also the issue of uncertainty associated with upscaling laboratory generated data ...
...SPE-192347-MS 9 Fig. 6 - Chemo-Mechanical Facies Classification. The Si is plotted separately due to closely range values. The numbers on the... the corresponding clusters. These CMF units are formation specific that systematically linked the rock composition, texture and ...mechanical properties. Each of the CMF unit will behave differently to stress changes in the ...
Abstract Geochemical analysis of rocks is fundamental to the understanding of geology and earth sciences. X-ray dispersive spectrometry and other automated techniques are increasingly being used to determined and quantify the abundances of the major, trace elements and other rock properties. This study utilized a combination of dispersive spectrometric techniques (MicroXRF) and impulse rebound hammer method to establish links between geochemical and mechanical properties of rocks through a non-destructive method. MicroXRF has high resolution and can detect trace elements within the parts per billion range. The micro-rebound hammer was used to generate a reduced Young's modulus (E*), which gives a measure of the rock strength with negligible impact on the rock itself. In order to explore, visualize and understand the dataset generated, principal component analysis (PCA) was applied to emphasize variation and bring out strong patterns in the dataset. The first two dimensions of PCA express 57.09% of the total dataset inertia; that means that 57.09% total variability in the data is explained by the planes/dimensions. The first dimension, which showed a strong positive correlation to clay forming minerals and rock strength, was tentatively identified as the clay gradient. The second dimension describes diagenetic alteration processes responsible for the enrichment of elements such as Ni, Mo etc. Further, a positive correlation was established between E* and four elements Cobalt (Co), Strontium (Sr), Titanium (Ti), and Zircon (Zr). Remarkably, Silicon (Si) had a negative correlation with all elements but positive correlation with porosity and permeability. We therefore identified Co, Ti, Sr, and Zr as proxy for the determination of rock strength specific for studied samples and proposed a workflow based on our sequences of analysis and interpretation. Furthermore, we identified four chemo-mechanical facies through hierarchical clustering of the product of the PCA. This presented methodology could be specifically useful for geomechanical characterization of rocks; a key requirement needed for in-situ stresses estimation, wellbore stability analysis, reservoir stimulation and compaction, pore pressure prediction, and more importantly for characterizing drill cuttings where size and time are limiting. Drilling operations require constantly evolving cost effective and time efficient techniques, the proposed workflow will serve these purposes i.e. rapid determination of elemental composition (microxrf) coupled with E*will give a reliable proxy for rock strength. The technique can be applied to, drill cuttings, slabs and whole core directly without prior sample preparation.
- Asia > Middle East > Saudi Arabia (0.29)
- Europe > Austria (0.28)
Maximizing the Use of Rock Mechanical Data through Empirical Correlation and Data-Driven Analytics
Khan, Khaqan (Saudi Aramco) | Almarri, Misfer (Saudi Aramco) | Al-Qahtani, Adel (Saudi Aramco) | Syed, Shujath Ali (Baker Hughes, a GE Company) | Negara, Ardiansyah (Baker Hughes, a GE Company) | Jin, Guodong (Baker Hughes, a GE Company)
...SPE-195140-MS Maximizing the Use of Rock Mechanical Data through Empirical Correlation and Data-Driven Analytics Khaqan Khan, Misfer Almarri, and Adel...ay not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright. Abstract Rock mechanical properties are required as an input in many petroleum engineering applications, such as borehole stability ana...prediction. Their determination is commonly from various laboratory testing performed on subsurface rock samples. Due to the scarcity of reservoir samples and test cost, ...
...dical-CT scans were performed on these samples to quantify the samples condition and quality before mechanical tests. Depending on the availability of samples at each depth, either UCS, SST or MST tests are con...heir medical-CT images. Sample preparation All samples (cylindrical in shape) were prepared for the mechanical testing following the ASTM standard D4543 with very few exceptions. Depending on the whole core siz...son's ratio (ฯ ) were calculated at 50% of the peak stress. Petrophysical measurements Petrophysical properties of each tested sample were measured using various experimental techniques. Bulk density is calculat...
...4 SPE-195140-MS Figure 1--Illustration of simultaneous measurement of static and dynamic elastic properties during a ...rock mechanical testing. Validating measured UCS data using published empirical correlations Static ...rock mechanical properties cannot be measured directly from downhole tools. The empirical correlations built from laboratory e...
Abstract Rock mechanical properties are required as an input in many petroleum engineering applications, such as borehole stability analysis, hydraulic fracturing design, and sand production prediction. Their determination is commonly from various laboratory testing performed on subsurface rock samples. Due to the scarcity of reservoir samples and test cost, rock mechanical data are always very limited. Therefore, empirical correlations are very often used to estimate the mechanical properties from downhole logging measurements. Alternatively, the data-driven analytics techniques have been developed for predicting rock properties from other formation properties that can be determined directly from logs. This paper presents a study of developing correlation equations and data-driven models that are used to predict the unconfined compressive strength (UCS) from logging data. Various rock mechanical tests including UCS, single- and multi-stage triaxial tests are performed on sandstone samples from three wells in one region. UCS values are obtained either from the UCS testing directly or from the Mohr-Coulomb failure analysis indirectly. Rock properties, such as mineralogy, porosity, grain and bulk density, ultrasonic wave velocities, are measured for each tested sample, which are used to build the correlations and data-driven analytical models for predicting UCS. Results shows that the empirical correlations are not universal and often cannot be used without some modifications, while the data-driven model is more generalized in application. In addition, data quality is very crucial for building correlations or predictive models.
- Europe (1.00)
- Asia > Middle East > Saudi Arabia (0.46)
- North America > United States > Colorado (0.28)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.49)
- North America > United States > Colorado > Piceance Basin > Williams Fork Formation (0.99)
- North America > United States > Texas (0.91)
...SPE-183513-MS Mechanical Anisotropy of Unconventional Shale - Build the Correct Relationship between Static and Dynamic ...Properties Guodong Jin, Syed Shujath Ali, and Ali Abdullah Al Dhamen, Baker Hughes Inc. Copyright 2016, Soci...asurements are only performed along the longitudinal axis of the sample, where the apparent dynamic properties are determined using isotropic media equations, and then used to build a correlation model between ...
...2 SPE-183513-MS Introduction Determination of unconventional shale mechanical properties (Young's modulus and Poisson's ratio) is crucial for reservoir exploration and development. Knowled...ge of these properties helps workers understand and determine the initiation of hydraulic fractures, locations of the micr...g, design of the well spacing and hydraulic fracturing (Franquet et al., 2011, Horne et al., 2012). Rock mechanical properties are usually measured in triaxial compression tests (static ...
...rabia. Frydmann, M., 2010, Determination of the dynamic elastic constants of a transverse isotropic rock based on borehole dipole sonic anisotropy in deviated wells. IBP2304_10, presented at the Rio Oil &... anisotropy on the Kimmeridge shale. SEG 2000 Expanded Abstracts. Sone, H. and Zoback, M. D., 2013, Mechanical properties of shale-gas reservoir rocks - Part 1: Static and dynamic elastic ...properties and anisotropy. Geophysics, Vol. 78, No. 5, P. D381-D392. Thomsen, L., 1986, Weak elastic anisotrop...
Abstract Most of unconventional shales are mechanically anisotropic and usually treated as transversely isotropic (TIV) media. Full anisotropy characterization of gas shale samples traditionally requires laboratory tests on several plugs cut along different orientations to the bedding. In practice, very often, this is untenable โ due to the scarcity of shale plugs, experimental challenges, cost consideration and other factors. As alternate, ultrasonic measurements are only performed along the longitudinal axis of the sample, where the apparent dynamic properties are determined using isotropic media equations, and then used to build a correlation model between static and dynamic properties. However, such approximations could result in significant difference in reservoir rock mechanical properties determined from downhole acoustic logging measurements when the simplified apparent dynamic/static correlation model is used. The paper performs ultrasonic measurements, velocity anisotropy analysis, and triaxial compression testing on a series of shale samples cut in two orientations โ parallel and perpendicular to the bedding. The static Young's moduli measured along the bedding are not always higher than those measured perpendicular to the bedding. For the samples tested, horizontal plugs generally have higher Young's moduli than vertical plugs in the range of low modulus, while lower than vertical plugs in the high modulus range. Generally, horizontal plugs have relatively higher static Poisson's ratios than vertical plugs. Both the dynamic and apparent dynamic Young's moduli are higher than the static moduli. There exist strong correlations between the dynamic/apparent dynamic and static moduli. However, the apparent dynamic moduli seem to have a relative better correlation with static values than the dynamic ones. In addition, the apparent dynamic moduli are higher than dynamic ones when they are measured perpendicular to the bedding, while they are almost the same when measured parallel to the bedding. The discrepancy observed between the dynamic (measured from velocity anisotropy analysis) and apparent dynamic (using the isotropic model) confirms the importance of distinguishing and carefully selecting between these values when building static-dynamic relations for log-core calibration. The method of performing velocity anisotropy analysis on one horizontal plug only enables one to obtain the complete dynamic properties for the transversely isotropic media, which could considerably simplify the anisotropic measurements, save core material, and make much more geomechanical data available for shale well development.
- Asia > Middle East > Saudi Arabia (0.28)
- North America > United States > West Virginia (0.28)
- Asia > Middle East > UAE > Abu Dhabi Emirate > Abu Dhabi (0.16)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geophysics > Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Reservoir geomechanics (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
Integrated Techniques for Continuous Rocks Elastic Properties Profile and Geomechanical Characterization
Hussain, Maaruf (Baker Hughes, a GE Company) | Amao, Abduljamiu (King Fahd University of Petroleum and Minerals) | Muqtadir, Arqam (King Fahd University of Petroleum and Minerals) | Al-Ramadan, Khalid (King Fahd University of Petroleum and Minerals) | Babalola, Lamidi (King Fahd University of Petroleum and Minerals) | Jin, Guodong (Baker Hughes, a GE Company)
...SPE-197169-MS Integrated Techniques for Continuous Rocks Elastic Properties Profile and Geomechanical Characterization Maaruf Hussain, Baker Hughes, a GE Company; Abduljamiu ...must contain conspicuous acknowledgment of SPE copyright. Abstract The knowledge of rocks elastic properties (REP) is crucial to geomechanical modeling throughout an asset life cycle. Reliable geomechanical m...Paleozoic tight sands and shale reservoirs units. The approach, which uniquely considered scales of rock geochemical and ...
...2 SPE-197169-MS Introduction The knowledge of rocks elastic properties (REP) is crucial to geology, mining, geophysics, geomechanics, and related fileds. It is a function...ssure, nonhydrostatic stress, and failure history (Belikov, 1962; Weidner, 1987). The overall rocks mechanical behavior to external stresses significantly depends on its elastic ...properties (Hussain et al., 2019). Thus, REP forms part of the primary input in geomechanical modeling (Maลkow...
...bilities (Gramin et al., 2016; Hussain et al., 2018) than Ed and Es. The geochemical signatures and mechanical properties of the shale unit is a classical example for demonstrating the sensitivity of E*. The Ed and Es sho... captured by E*, whereas not so apparent in Ed and Es. Figure 2--Showing variation of the obtained mechanical properties with measured elemental data and the lithology unit. The E* values suggest the clay minerals has i...nfluence on mechanical properties of the ...
Abstract The knowledge of rocks elastic properties (REP) is crucial to geomechanical modeling throughout an asset life cycle. Reliable geomechanical models requires calibration of well log REP with core measurements. However, sample availability, representativeness, time, and cost are problems associated with core measurements. In this paper, we integrated REP derived from two laboratory techniques performed on several core covering over 800 ft interval samples from Paleozoic tight sands and shale reservoirs to obtain a continuous REP profile for better- upscaling of reservoir model parameters. For lithology delineation, intact core samples were scanned utilizing MicroXRF. While, REP was measured using Autoscan and AutoLab systems. The Autoscan employs non- destructive technique to characterize the variability of REP. The AutoLab uses the standard triaxial testing method to provide REP at reservoir conditions. The results were tabulated and statistically treated to establish significant empirical relationships. REP derived from triaxial tests on selected samples include, the P and S wave velocities (Vp and Vs), static moduli (Youngโs modulus (Es) and Poissonโs ratio (vs)), as well as dynamic moduli (Youngโs modulus (Ed) and Poissonโs ratio (vd)). While the reduced Youngโs modulus (E*) was obtained from non- destructive method. Lithofacies were established from elemental data. The E* reveals details of several geomechanical heterogeneity and anisotropy which are not possible with traditional triaxial method. There is a significant correlation between E* and Es, Ed, Vs, and Vp. A continuous REP profile was developed using E* with geochemical data. Based on the characterized profile, fracture height growth barriers identified were toughness/modulus and interface barriers. These can significantly affect hydraulic fracture vertical growth within the studied Paleozoic tight sands and shale reservoirs units. The approach, which uniquely considered scales of rock geochemical and mechanical properties and data analytics, demostrate the possibility of generating a continuous REP profile using laboratory aquired dataset. Thus, the difficulty associated with geomechanical characterization and model calibration of highly laminated unconventional reservoirs using actual laboratory data is resolved. This has a directly implication to both conventional and unconventional geomechanical modeling, where the determination of upscaled-reservoir model parameters matters.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
...SPE-192304-MS Geomechanical Property Computation from Digital Rock Models and Comparison with Core Measurements Bilal Saad, Guodong Jin, Shujath Syed Ali, Elham Alsh...ay not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright. Abstract Rock mechanical properties are critical to reduce drilling risk and maximize well and reservoir productivity. This paper prese...nt a methodology of predicting mechanical properties (Young's modulus and Poisson's ratio) from 3D ...
...SPE-192304-MS 2 technology enables to obtain a "realistic" 3D digital model of a reservoir rock (Knackstedt et al., 2004, Arns and Meleรกn, 2009, Grader et al., 2009, and the references therein), ...which provides a physical boundary for computing various physical properties of reservoir rocks (Jin et al., 2007). An alternative approach of obtaining digital rocks is to gen... or indirectly from downhole logging measurements, which is the focus of this study. Computation of mechanical properties of a random, multi-mineral phase heterogeneous ...
...b) Fig. 1 -- Left: Computer-generated initial grain packing for sample A. Right: Computer-generated rock model. Colors denote the different mineralogy of grains, cement and clay. ...Mechanical property computation A 3D computer-generated ...rock model provides the microstructure and boundary for simulating various physics phenomena and computi...
Abstract Rock mechanical properties are critical to reduce drilling risk and maximize well and reservoir productivity. This paper present a methodology of predicting mechanical properties (Young's modulus and Poisson's ratio) from 3D rock models generated using laboratory measurements or downhole logging data. The 3D rock model provides the microstructure and boundary to simulate rock elastic properties. Mechanical properties are computed from rock models using the finite element method. Laboratory measurements were conducted on four Berea sandstone samples to determine the mechanical properties for comparison and other properties as input in rock modeling, such as bulk and grain density, porosity, mineralogy, and grain-size distributions. Numerical results from rock models generally match the core measurements of the corresponding samples. The methodology proposed in this study could potentially be applied downhole for predicting the mechanical property profile along the wellbore, as all input parameters to generate rock models can be derived from logging measurements.
- North America > United States > California (0.28)
- North America > United States > West Virginia (0.26)
- North America > United States > Pennsylvania (0.26)
- (2 more...)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.37)
...SPWLA 58 th Annual Logging Symposium, June 17-21, 2017 presented at the 47 th U.S. Rock was involved in quantum ...mechanical modelling of the Mechanics/Geomechanics Symposium, San ultrafast dynamics phenomena and thermal con...n Magnitude in Deep Wells, International Journal of and evaluation, logging interpretation, digital rock Rock Mechanics and Mining Sciences, 40(7-8), physics, geomechanics and core analysis. Prior to his 1049-...
...17-21, 2017 DATA-DRIVEN BRITTLENESS INDEX PREDICTION FROM ELEMENTAL SPECTROSCOPY AND PETROPHYSICAL PROPERTIES USING SUPPORT-VECTOR REGRESSION Ardiansyah Negara, Syed Shujath Ali, Ali Al Dhamen, Hasan Kesserwa..., 2017. In total, 28 cases were run with different combinations of petrophysical and mineralogical properties, number ABSTRACT of training dataset, and SVR kernel functions. The results reveal that the SVR-ba...Brittleness index is one of the critical geomechanical match very well with the laboratory-measured properties to understand the ...
...e estimated from conventional by Shi et al. (2016b) to estimate brittleness index from well logs or rock mineralogical composition. Brittleness well logs using artificial intelligence techniques. In the m...ved from the Longmaxi marine shale gas reservoir in the Sichuan empirical correlation specific to a rock type. Basin, China. A comparison of the BP-ANN and LS-SVR models demonstrate that the latter is mor...machines, support-vector machines) have been proposed and used As mentioned earlier, brittleness of rock is a complex intensively to predict ...
ABSTRACT Brittleness index is one of the critical geomechanical properties to understand the rock's drillability in drilling operations and screen effective hydraulic fracturing candidates in unconventional reservoirs. Brittleness index can generally be obtained from stress or strain based relationships. It can also be estimated from conventional well logs or rock mineralogical composition. Brittleness index measurements from stress/strain based relationships require laboratory tests, which are time-consuming and core samples are available only at discrete depths. While well logs can estimate a continuous profile of brittleness index along the borehole, it is derived from empirical correlation specific to a rock type. More recent advancements in logging tools have enabled the determination of elemental spectroscopy downhole. This information combined with petrophysical properties such as density and porosity can capture brittleness characteristics of rocks. This paper presents the use of support-vector regression (SVR) to construct a data-driven brittleness index prediction from the elemental spectroscopy and petrophysical properties. The relationship of brittleness index with elemental spectroscopy, density, and porosity is often complex and nonlinear. The SVR described in this paper is used to correlate the elemental spectroscopy, density, and porosity to the brittleness index, thereafter building a data-driven brittleness index prediction model. The dataset of brittleness index, elemental spectroscopy, density, and porosity used in this study are based on various geological formations. Laboratory tests such as unconfined compressive strength, confined compressive strength, and Brazilian test were conducted. Brittleness indices were calculated based on data generated from these tests. Elemental spectroscopy data were obtained from X-ray fluorescence (XRF) analysis. The data are then separated into two categories: training and testing data. Training data are used to train the SVR and establish the brittleness index prediction model, while the testing data are used for validation. In total, 28 cases were run with different combinations of petrophysical and mineralogical properties, number of training dataset, and SVR kernel functions. The results reveal that the SVR-based brittleness indices match very well with the laboratory-measured brittleness indices. Cross-correlation plots of regression models between the predicted and the measured brittleness indices show high values of coefficient of determination. The small error and high values of coefficient of determination denote the SVR models' good performances. The prediction accuracy improves as more data are included to train the algorithm. From the comparison of SVR-kernel-function-based models, we observe that the RBF-based model performs better than the polynomial-based model. The RBF-based model yields better accuracy than the polynomial-based model using the same number of training dataset. Referring to the RBF-based model with 80% training dataset, it was observed that elemental spectroscopy has more influence than the other rock properties on the prediction. The promising results stemming from this study confirm that SVR can be further applied to build a brittleness index prediction model based on mineralogy logs and petrophysical logs.
- North America > United States (1.00)
- Europe (1.00)
- Asia (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.48)
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- Asia > China > Sichuan > Sichuan Basin (0.99)
Enhancing Rock Mechanical Characterization - New Approach To Quantitatively Determine the Imminent Failure State During Multi-Stage Triaxial Testing
Ali, Syed Shujath (Baker Hughes, a GE company) | Jin, Guodong (Baker Hughes, a GE company) | Al Dhamen, Ali Abdullah (Baker Hughes, a GE company) | Saad, Bilal (Baker Hughes, a GE company)
...SPWLA 59 th Annual Logging Symposium, June 2-6, 2018 ENHANCING ROCK MECHANICAL CHARACTERIZATION - NEW APPROACH TO QUANTITATIVELY DETERMINE THE IMMINENT FAILURE STATE DURING MULTI...tion at the SPWLA 59 th Annual Logging Symposium held in London, UK, June 2-6, 2018. Reservoir rock failure criteria are required as an input in many petroleum engineering applications, ABSTRACT suc...remely viable for the determination of Coulomb failure envelope (hereafter referred to as reservoir rock failure criteria in the case of limited only failure envelope for simplicity), which core samples a...
...upplier. Their permeability is about 50 mD, given by the stone supplier. Table 1 lists the physical properties of plugs and their type of tests. For test type, MSTM denotes the MST test using MVS unloading crit...ors were due to light. Berea sandstone B50-1 at the confining pressure of 591 psi. Table 1 Physical properties of Berea sandstone samples. RSG varies with time during the ...rock compression D - diameter, L - length, - bulk density, - gr ain test. Its critical value is...
...nd -9c. As the SST and MSTR test data. shown in Fig. 4, these samples were cut from the same long rock. Their physical ...properties and Parameter SST MSTR Difference (%) mineralogy composition are almost identical Cohesion 2087 ...deviatoric stress. Considering the heterogeneity of SST test of sample B50-7c (Table 5). For sample rock samples, the failure envelope from the MSTR B50-8c, its RSG value at the peak stress is -88.0, test...
ABSTRACT This paper presents a new unloading criterion for enhancing the multi-stage triaxial (MST) testing, which is extremely viable for the determination of reservoir rock failure criteria in the case of limited core samples available. The method continuously monitors the change of radial-strain gradient (RSG) in real time, and uses it to quantitatively determine the stress state immediately prior to rock failure during a MST test. RSG is defined as the ratio of change of radial strain to change of the time, whose value is closely associated with the stress state of a compressed sample. The proposed RSG method is tested and validated on Berea sandstone and tight sand samples. The failure envelope from the single-stage triaxial (SST) testing is used as the benchmark for comparison of the MST test data. For each rock type, irrespective of the applied confining pressure, samples fail at almost the same RSG value, which provides the guideline of selecting a RSG value as the unloading point in a MST test. Failure envelopes from the MST test using the RSG criterion matches very well with those from SST tests. The RSG method can be implemented practically on the most available instruments, which usually measure the radial strain during the testing. The method decides quantitatively and consistently the unloading point for each stage during a MST test so as to avoid an early stop or breaking the plug before the last stage. With few samples available, the method enables a more accurate determination of failure parameters from MST testing that are important for many applications such as reservoir stress-state determination, log dynamic-static correlation, wellbore stability, and hydraulic fracturing design. INTRODUCTION Reservoir rock failure criteria are required as an input in many petroleum engineering applications, such as borehole stability analysis (Manshad et al., 2014), sand production prediction (Javani et al., 2017), optimum mud design (Gholami et al., 2014). A common criterion is known as Mohr-Coulomb failure envelope (hereafter referred to as only failure envelope for simplicity), which predicts the occurrence of failure if the stress state (or the Mohr circle) is tangent to the failure envelope. Determination of the failure envelope usually requires to conduct several single-stage triaxial (SST) tests on at least three rock samples or one multi-stage triaxial (MST) test on one single sample at various confining pressures. When rock samples are scarce, MST test is often the only option used for determining the failure envelope.
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- Geology > Geological Subdiscipline > Geomechanics (1.00)
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- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.37)
...SPE-183106-MS Enhancing Rock Property Prediction from Conventional Well Logs Using Machine Learning Technique - Case Studies of ...remains central to any asset development. Interpretation is limited by our current understanding of rock-fluid physics in source rocks, which is still developing. The gap is clearly evident in unconventio...nal source rock interpretation where approximations such as pseudo-Archie approach are used for saturation estimati...
...(samples) to build a hyper-regression relationship. This technique can be easily adapted to predict rock mechanical properties and especially useful for unconventional reservoirs where traditional models may not be applicable ...
...oir deliverability and hydrocarbon potential, respectively. Permeability is a property of reservoir rock that controls the fluid flow. TOC is the amount of organic carbon present in a ...rock that determines the ...rock's ability to generate hydrocarbons. In general, permeability and TOC are measured in the laboratory...
Abstract It is timely for our industry to introspect on ways for step improvement in the utility of the wireline and logging-while-drilling logs which remains central to any asset development. Interpretation is limited by our current understanding of rock-fluid physics in source rocks, which is still developing. The gap is clearly evident in unconventional source rock interpretation where approximations such as pseudo-Archie approach are used for saturation estimation. This paper presents the use of emerging knowledge in machine learning to demonstrate its applicability for improving the total organic carbon (TOC) estimation in an unconventional well and permeability prediction in a conventional well. We have used the support-vector regression (SVR) technique, which is a new machine learning technique. Vast amount of logging data can be quickly processed using this technique. Limited core data is used to train the SVR algorithm. In this work, we first use the SVR technique to establish a correlation between conventional well logs (e.g., gamma ray, formation resistivity, neutron porosity, bulk density) and core measurements, thereafter building a rock property-prediction model as a function of well logs selected. Two field datasets from a South American well and a Mississippi Canyon well were selected to validate the method. Both wells contain a suite of logs and few core TOC and permeability data. Various combinations of conventional well logs were studied to check if the prediction accuracy can be improved. The results of the two case studies reveal that the SVR technique provides accurate and reliable TOC and permeability predictions. We observed in the case study of TOC prediction that incorporating the carbon weight fraction log as an input, in addition to the conventional well logs, improves the TOC prediction because the carbon weight fraction log provides information about the amount of carbon, which eventually helps the SVR algorithm to learn better from the data. Meanwhile, for the case study of permeability prediction, we observed that the conventional well logs are sufficient to generate a good permeability prediction model. Additional logging data including the nuclear-magnetic-resonance (NMR) logging data do not improve significantly the prediction accuracy. In conclusion, SVR technique could be used to improve our log interpretation. This technique can be easily adapted to predict rock mechanical properties and especially useful for unconventional reservoirs where traditional models may not be applicable and new methods are still evolving. Such new data analysis technologies could optimize our logging service and core analysis planning.
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- Geology > Rock Type > Sedimentary Rock (1.00)
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- North America > United States > Wyoming > Uinta Basin (0.99)
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