scholarly journals A Review of Brittleness Index Correlations for Unconventional Tight and Ultra-Tight Reservoirs

Geosciences ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 319 ◽  
Author(s):  
Mews ◽  
Alhubail ◽  
Barati
Keyword(s):  
Clay Content ◽  
Detailed Comparison ◽  
Unconventional Reservoirs ◽  
Brittleness Index ◽  
Tight Reservoirs ◽  
Correlation Parameters ◽  
Rock Brittleness ◽  
Universal Correlation ◽  
The U.S

Rock brittleness is pivotal in the development of the unconventional reservoirs. However, the existence of various methods of calculating the brittleness index (BI) such as the mineral-based brittleness index (MBI), the log-based brittleness index (LBI), and the elastic-based brittleness index (EBI) lead to inconclusive estimations of the brittleness index. Hence, in this work, the existing correlations are applied on prolific unconventional plays in the U.S. such as the Marcellus, Bakken, Niobrara, and Chattanooga Formation to examine the various BI methods. A detailed comparison between the MBI, LBI, and EBI has also been conducted. The results show that a universal correlation cannot be derived in order to define brittleness since it is a function of lithology. Correlation parameters vary significantly from one shale play to another. Nevertheless, an overall trend shows that abundant quartz and carbonates content yield high brittleness values, while the high clay content and porosity lower the rock brittleness.

scholarly journals Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index

2020 ◽  
Vol 10 (5) ◽  
pp. 1691 ◽  
Author(s):  
Deliang Sun ◽  
Mahshid Lonbani ◽  
Behnam Askarian ◽  
Danial Jahed Armaghani ◽  
Reza Tarinejad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Point Load ◽  
P Wave ◽  
Machine Learning Techniques ◽  
Brittleness Index ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Ann Model ◽  
Learning Techniques ◽  
Rock Brittleness

Despite the vast usage of machine learning techniques to solve engineering problems, a very limited number of studies on the rock brittleness index (BI) have used these techniques to analyze issues in this field. The present study developed five well-known machine learning techniques and compared their performance to predict the brittleness index of the rock samples. The comparison of the models’ performance was conducted through a ranking system. These techniques included Chi-square automatic interaction detector (CHAID), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), and artificial neural network (ANN). This study used a dataset from a water transfer tunneling project in Malaysia. Results of simple rock index tests i.e., Schmidt hammer, p-wave velocity, point load, and density were considered as model inputs. The results of this study indicated that while the RF model had the best performance for training (ranking = 25), the ANN outperformed other models for testing (ranking = 22). However, the KNN model achieved the highest cumulative ranking, which was 37. The KNN model showed desirable stability for both training and testing. However, the results of validation stage indicated that RF model with coefficient of determination (R2) of 0.971 provides higher performance capacity for prediction of the rock BI compared to KNN model with R2 of 0.807 and ANN model with R2 of 0.860. The results of this study suggest a practical use of the machine learning models in solving problems related to rock mechanics specially rock brittleness index.

A Convenient Technology to Calculate Geomechanical Properties From Drilling Data

2020 ◽  
Author(s):  
Mazeda Tahmeen ◽  
Geir Hareland ◽  
John P. Hayes
Keyword(s):  
Young’S Modulus ◽  
Poisson’S Ratio ◽  
Young's Modulus ◽  
Poisson's Ratio ◽  
Brittleness Index ◽  
Eagle Ford ◽  
Horizontal Drilling ◽  
Geomechanical Properties ◽  
Drilling Data ◽  
Rock Brittleness

Abstract The multistage hydraulic fracturing is the best practice to stimulate unconventional hydrocarbon reservoirs for optimal production. Recent studies suggested that selective stimulation design could significantly increase production rates at a reduced cost rather than using non-selective geometric stages. An optimal design needs detailed logging and core information to selectively perforate and optimize the stimulation treatment. In most cases, the non-selective evenly spaced geometric stimulation design is used, primarily due to the time consuming and expensive conventional logging tools and techniques. In this article, a 3D wellbore friction model is used to estimate the effective downhole weight on bit (DWOB) from the drilling data, directional survey data and drill string information. The estimated DWOB is used as an input to the inverted rate of penetration (ROP) model along with other drilling data, drill bit specifications and reservoir specific formation constants, to calculate rock mechanical and reservoir properties including, compressive strength, Young’s modulus, porosity, permeability and Poisson’s ratio without the use of expensive downhole logging tools. The rock brittleness index is calculated from the relationship between Young’s modulus and Poisson’s ratio based on the definitions of rock brittleness used in recent years. The field data from horizontal drilling of three sample wells were used to investigate the geomechanical properties in the Montney shale formation and the lower Eagle Ford formation in North America. The calculated geomechanical properties were compared to the corresponding test analysis on cores. The authors investigated the rock brittleness index from the sample well data drilled horizontally in the lower Eagle Ford formation. This novel technology could help geologists and reservoir engineers better exploit unconventional reservoirs leading to optimal selective stimulations and greater net present value (NPV).

scholarly journals Hurricane Maria in the U.S. Caribbean: Disturbance Forces, Variation of Effects, and Implications for Future Storms

2018 ◽  
Vol 10 (9) ◽  
pp. 1386 ◽  
Author(s):  
Ashley Van Beusekom ◽  
Nora Álvarez-Berríos ◽  
William Gould ◽  
Maya Quiñones ◽  
Grizelle González
Keyword(s):  
Forest Fragmentation ◽  
Vegetation Index ◽  
Forest Cover ◽  
Clay Content ◽  
Luquillo Experimental Forest ◽  
Landslide Occurrence ◽  
Landscape Characteristics ◽  
The Third ◽  
The Impact ◽  
The U.S

The impact of Hurricane Maria on the U.S. Caribbean was used to study the causes of remotely-sensed spatial variation in the effects of (1) vegetation index loss and (2) landslide occurrence. The vegetation index is a measure of canopy ‘greenness’, a combination of leaf chlorophyll, leaf area, canopy cover and structure. A generalized linear model was made for each kind of effect, using idealized maps of the hurricane forces, along with three landscape characteristics that were significantly associated. In each model, one of these characteristics was forest fragmentation, and another was a measure of disturbance-propensity. For the greenness loss model, the hurricane force was wind, the disturbance-propensity measure was initial greenness, and the third landscape characteristic was fraction forest cover. For the landslide occurrence model, the hurricane force was rain, the disturbance-propensity measure was amount of land slope, and the third landscape characteristic was soil clay content. The model of greenness loss had a pseudo R2 of 0.73 and showed the U.S. Caribbean lost 31% of its initial greenness from the hurricane, with 51% lost from the initial in the Luquillo Experimental Forest (LEF) from Hurricane Maria along with Hurricane Irma. More greenness disturbance was seen in areas with less wind sheltering, higher elevation and topographic sides. The model of landslide occurrence had a pseudo R2 of 0.53 and showed the U.S. Caribbean had 34% of its area and 52% of the LEF area with a landslide density of at least one in 1 km2 from Hurricane Maria. Four experiments with parameters from previous storms of wind speed, storm duration, rainfall, and forest structure over the same storm path and topographic landscape were run as examples of possible future scenarios. While intensity of the storm makes by far the largest scenario difference, forest fragmentation makes a sizable difference especially in vulnerable areas of high clay content or high wind susceptibility. This study showed the utility of simple hurricane force calculations connected with landscape characteristics and remote-sensing data to determine forest susceptibility to hurricane effects.

scholarly journals Quantitative seismic interpretation of rock brittleness based on statistical rock physics

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. IM63-IM75 ◽  
Author(s):  
Lin Wang ◽  
Feng Zhang ◽  
Xiang-Yang Li ◽  
Bang-Rang Di ◽  
Lian-Bo Zeng
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Rock Physics ◽  
Seismic Interpretation ◽  
Brittleness Index ◽  
Simple Relationship ◽  
Elastic Parameters ◽  
Markov Random ◽  
Rock Brittleness ◽  
Statistical Rock Physics

Rock brittleness is one of the important properties for fracability evaluation, and it can be represented by different physical properties. The mineralogy-based brittleness index (BIM) builds a simple relationship between mineralogy and brittleness, but it may be ambiguous for rocks with a complex microstructure; whereas the elastic moduli-based brittleness index (BIE) is applicable in the field, but BIE interpretation needs to be constrained by lithofacies information. We have developed a new workflow for quantitative seismic interpretation of rock brittleness: Lithofacies are defined by a criterion combining BIM and BIE for comprehensive brittleness evaluation; statistical rock-physics methods are applied for quantitative interpretation by using inverted elastic parameters; acoustic impedance and elastic impedance are selected as the optimized pair of attributes for lithofacies classification. To improve the continuity and accuracy of the interpreted results, a Markov random field is applied in the Bayesian rule as the spatial constraint. A 2D synthetic test demonstrates the feasibility of the Bayesian classification with a Markov random field. This new interpretation framework is also applied to a shale reservoir formation from China. Comparison analysis indicates that brittle shale sections can be efficiently discriminated from ductile shale sections and tight sand sections by using the inverted elastic parameters.

Identification of brittle/ductile areas in unconventional reservoirs using seismic and microseismic data: Application to the Barnett Shale

2015 ◽  
Vol 3 (4) ◽  
pp. T233-T243 ◽  
Author(s):  
Roderick Perez Altamar ◽  
Kurt J. Marfurt
Keyword(s):  
Clay Content ◽  
Elastic Parameter ◽  
Unconventional Reservoirs ◽  
Seismic Survey ◽  
Parameter Estimates ◽  
Hydraulic Fractures ◽  
Seismic Acquisition ◽  
Microseismic Events ◽  
3D Volume ◽  
Horizontal Wellbores

Brittleness in unconventional reservoirs is mainly controlled by mineralogy, and it increases with quartz and dolomite content, whereas an increase in the clay content represents an increase in ductility. To generate regional brittleness maps, we have correlated the mineralogy-based brittleness index to elastic parameters measured from well logs. This correlation can then be used to predict the brittleness from surface seismic elastic parameter estimates of [Formula: see text] and [Formula: see text]. We applied the workflow to a 3D seismic survey acquired in an area where more than 400 wells were drilled and hydraulically fractured prior to seismic acquisition. Combining [Formula: see text] and [Formula: see text] into a single 3D volume allowed the combination of both attributes into a single 3D volume, which can be converted to brittleness using a template based on the well log and core data. Neither of these seismic estimates were direct measures of reservoir completion quality. We, therefore, used production logs and extracted surface seismic estimates at microseismic event locations to analyze the completion effectiveness along several horizontal wellbores in the reservoir. We defined four petrotypes in [Formula: see text] and [Formula: see text] space depending on their brittleness and gas saturation, and we found that most of the microseismic events fell into the zone described as brittle in the [Formula: see text]-[Formula: see text] crossplots. These observations supported the well-known idea that regardless of where the well was perforated, microseismic events appeared to preferentially grow toward the more brittle areas, suggesting the growth of hydraulic fractures into the brittle petrotype.

scholarly journals An Evaluation Method of Rock Brittleness Based on the Prepeak Crack Initiation and Postpeak Stress Drop Characteristics

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Meiben Gao ◽  
Tianbin Li ◽  
Lubo Meng
Keyword(s):  
Crack Initiation ◽  
Stress Drop ◽  
Evaluation Method ◽  
Triaxial Compression ◽  
Experimental Tests ◽  
Confining Pressure ◽  
Brittleness Index ◽  
Comparison Results ◽  
Confining Pressures ◽  
Rock Brittleness

Recent research shows that the brittleness of rock is closely related to the initiation and propagation of internal microcracks, but there are few brittleness evaluation indices considering the characteristics of rock initiation. Based on the theoretical analysis of brittleness and the characteristics of rock initiation, this study proposes an evaluation method of rock brittleness based on the prepeak crack initiation and postpeak stress drop characteristics. First, based on the description and definition of brittleness by George Tarasov and Potvin et al., the feasibility of an evaluation method based on the prepeak crack initiation and postpeak stress drop is theoretically analyzed. Second, the component Bi representing the prepeak brittleness of rock and component Bii representing the prepeak brittleness of rock are constructed, and the product of the two is the brittleness index BI, representing the prepeak crack initiation and postpeak stress drop. Finally, experimental tests of granite and marble were conducted to evaluate the new index, and the brittleness indices of different methods are calculated and compared. The results show that, like other brittleness indices (B1∼B5), the brittleness index BI can effectively reflect the effects of different confining pressures and loading modes on rock brittleness. The brittleness of marble decreases with increasing confining pressure from 5 MPa to 35 MPa. At a confining pressure of 5 MPa, the brittleness of granite during a triaxial unloading test is greater than that during a triaxial compression test. The calculated results are consistent with the experimental results. By tests and comparison results, the reliability of this evaluation method was verified, which provides a way to evaluate rock brittleness from the perspective of crack initiation and is helpful to enrich the analysis and evaluation of rock brittleness in the laboratory.

scholarly journals Shale Oil/Gas: A New Opportunity for New Zealand?

2021 ◽  
Author(s):  
◽  
Raul Correa Rechden Filho
Keyword(s):  
New Zealand ◽  
Shale Gas ◽  
Oil And Gas ◽  
East Coast ◽  
Unconventional Reservoirs ◽  
Field Size ◽  
Brittleness Index ◽  
Rock Properties ◽  
Shale Oil ◽  
Geochemical Analyses

<p>Within New Zealand the East Coast Basin encompasses the primary shale oil and gás (unconventional) play areas in which both the Waipawa and Whangai formations are widespread. These formations are oil and gas prone and prevalent throughout a large area of the East Coast Basin. To characterise these two formations and evaluate their shale oil and gas potential, existing analytical results were supplemented by a set of new sample analyses of organic and inorganic geochemistry, and rock properties. Thus, some 242 samples from the Whangai Formation have organic geochemical analyses and 40 have inorganic geochemical analyses; for the Waipawa Formation there are 149 organic and 9 inorganic geochemical analyses. In addition, downhole logs from three exploration wells have been used to calculate the brittleness index of the Whangai Formation. All these data have been grouped by structural block and used to determine where the sweet spots are in each formation. Both basic and more robust statistical analysis (machine-learning) is applied to identify the best prospective area. The Rakauroa Member (Whangai Formation) and the Waipawa Formation have the best rock characteristics as unconventional reservoirs, based on quantity and quality. Maturation appears to be an issue for these formations, although there are some localised areas where the Whangai Formation has better maturity. The brittleness index is calculated only for the Rakauroa Member, given the lack of data available for other members of the Whangai Formation and the Waipawa Formation, and yielded promising results. The Motu block appears to be the best area in which to explore for unconventional oil and gas. The prospective resource volumes for the best case scenario for the Whangai (Rakauroa Member) and Waipawa formations combined in the Motu Block are 17% higher (713MMbbl) than the 2P (proved + probable) reserves of New Zealand for oil and condensate (588MMbbl) and 26% (2.1TCF) of the 2P (proved + probable) reserves of natural gas (7.8 TCF). Economic analysis shows feasibility to explore these unconventional reservoirs for both shale oil or shale gas with an oil price of US$60 for both methodologies tested. However, the methodology applied using standard shale oil and gas assessments shows feasibility only for shale oil. Shale gas would not be economic, unless a higher oil prices, lower costs or a technology was developed to improve the recovery factor of these reservoirs. These results indicate a minimum economic field size of 4.5 km² for this area.</p>

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