Data-driven optimization of brittleness index for hydraulic fracturing

Evaluation of brittleness index (BI) is a fundamental principle of a hydraulic fracturing design. A wide variety of BI calculations often baffle field engineers. The traditional value comparison may also not make the best of BI. Moreover, it is often mixed up with the fracability in field applications, thus causing concerns. We, therefore, redefine fracability as the fracturing pressure under certain rock mechanical (mainly brittleness), geological and injecting conditions to clarify the confusion. Then, we propose a data-driven workflow to optimize BIs by controlling the geological and injecting conditions. The machine learning (ML) workflow is employed to predict the fracability (fracturing pressure) based on field measurement. Three representative ML algorithms are applied to average the prediction, aiming to restrict the interference of algorithm performances. The contribution of brittleness on pressure/fracability prediction by error analysis (rather than the traditional method of BI-value comparison) is proposed as the new criterion for optimization. Six classic BI correlations (mineral-, logging- and elastic-based) are evaluated, three of which are optimized for the derivation of a new BI using the backward elimination strategy. The stress ratio (ratio of minimum and maximum horizontal principal stress), representing the geological feature, is introduced into the derived calculation based on the independent variable analysis. The reliability of the new BI is verified by error analyses using data of eight fracturing stages from seven different wells. Approximately 40%~50% of the errors are reduced based on the new BI. The differences among the performances of algorithms are also significantly restrained. The new brittleness index provides a more reliable option for evaluating the brittleness and fracability of the fracturing formation. The machine learning workflow also proposes a promising application scenario of the BI for hydraulic fracturing, which makes more efficient and broader usages of the BI compared with the traditional value comparison.

A New Rock Brittleness Evaluation Method Based on the Complete Stress-Strain Curve

Brittleness is a crucial parameter of rock mass and the key indicator in rock engineerings, such as rockburst prediction, tunnelling machine borehole drilling, and hydraulic fracturing. To solve the problem of using present brittleness indexes, the existing rock brittleness indexes were firstly summarised in this paper. Then, a brittleness index (BL), which considers the ratio of stress drop rate and stress increase rate and the peak stress, was proposed. This new index has the advantage of simplifying the acquisition of key parameters and avoiding dimensional problems, as well as taking the complete stress-strain curves into account. While applying the BL, the peak strain is used to describe the difficulty of brittle failure before the peak point, and the ratio of stress drop to strain increase can reflect the stress drop rate without dimension problem. In order to verify the applicability of BL, through the PFC2D, the microparameters and confining pressure were changed to model different types of rock numerical specimens and different stress condition. The results show that the BL can well reflect and classify the brittleness characteristics of different rock types and characterise the constraint of confining pressure on rock brittleness. Moreover, the influence of microparameter on macroparameter was studied. In order to further verify the reliability of the brittleness index (BL), this study conducted uniaxial and triaxial compression tests (30 MPa) on marble, sandstone, limestone, and granite under different confining pressure.

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

<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>

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

<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>

Study on Mechanical Behavior and Seepage Characteristics of Coal Mass during Unloading

With the increase of buried depth, the content of gas increases gradually. The gas in the mining process will lead to gas gush and other dynamic disasters, or even coal and gas gushing in front of the working face. Therefore, the study on the permeability distribution of coal and the surrounding rock is the core work of coal and gas mining at the same time. To study the mechanical behaviors and seepage characteristics of coal mass during unloading is to prepare for coal and gas mining in the future, which can not only ensure the safety of operators to the maximum extent but also increase the mining rate as much as possible. Based on the stress-strain curve and seepage curve, the brittleness index and seepage characteristics of coal are analyzed. The greater the brittleness index is, the more likely the coal mass is to produce cracks, and then to form large cracks, or even fracture. Through the study of brittleness index and seepage characteristics of coal mass, the mechanical behavior of coal mass can be easily obtained, so as to guide the mining of coal mass.

Utilizing Data-Driven Models to Predict Brittleness in Tuscaloosa Marine Shale: A Machine Learning Approach

The Tuscaloosa Marine Shale (TMS) formation is a clay- and liquid-rich emerging shale play across central Louisiana and southwest Mississippi with recoverable resources of 1.5 billion barrels of oil and 4.6 trillion cubic feet of gas. The formation poses numerous challenges due to its high average clay content (50 wt%) and rapidly changing mineralogy, making the selection of fracturing candidates a difficult task. While brittleness plays an important role in screening potential intervals for hydraulic fracturing, typical brittleness estimation methods require the use of geomechanical and mineralogical properties from costly laboratory tests. Machine Learning (ML) can be employed to generate synthetic brittleness logs and therefore, may serve as an inexpensive and fast alternative to the current techniques. In this paper, we propose the use of machine learning to predict the brittleness index of Tuscaloosa Marine Shale from conventional well logs. We trained ML models on a dataset containing conventional and brittleness index logs from 8 wells. The latter were estimated either from geomechanical logs or log-derived mineralogy. Moreover, to ensure mechanical data reliability, dynamic-to-static conversion ratios were applied to Young's modulus and Poisson's ratio. The predictor features included neutron porosity, density and compressional slowness logs to account for the petrophysical and mineralogical character of TMS. The brittleness index was predicted using algorithms such as Linear, Ridge and Lasso Regression, K-Nearest Neighbors, Support Vector Machine (SVM), Decision Tree, Random Forest, AdaBoost and Gradient Boosting. Models were shortlisted based on the Root Mean Square Error (RMSE) value and fine-tuned using the Grid Search method with a specific set of hyperparameters for each model. Overall, Gradient Boosting and Random Forest outperformed other algorithms and showed an average error reduction of 5 %, a normalized RMSE of 0.06 and a R-squared value of 0.89. The Gradient Boosting was chosen to evaluate the test set and successfully predicted the brittleness index with a normalized RMSE of 0.07 and R-squared value of 0.83. This paper presents the practical use of machine learning to evaluate brittleness in a cost and time effective manner and can further provide valuable insights into the optimization of completion in TMS. The proposed ML model can be used as a tool for initial screening of fracturing candidates and selection of fracturing intervals in other clay-rich and heterogeneous shale formations.

Evaluation Approach of Rock Brittleness Index for Fracturing Acidizing Based on Energy Evolution Theory and Damage Constitutive Relation

Acidizing, as an essential approach for well stimulation of sandstone or carbonate reservoirs, greatly affects the brittleness of the rock mass. Therefore, it is of great significance to develop a scientific brittleness evaluation methodology for the acid-corroded rock. In this paper, firstly, a damage constitutive model considering the compression hardening process of the acid-corroded sandstone under uniaxial loading is established and verified. Then, the evolution formulae of the relevant mechanical and fitting parameters are derived, and the stress-strain curve of the sandstone subjected to acid corrosion with soaking time is predicted. Finally, a theoretical model for evaluating the brittleness index (BI) of the acid-corroded sandstone based on energy evolution theory and damage constitutive relation is proposed. Based on this model, the BI of the sandstone subjected to acid corrosion is calculated and analyzed, and the BI of the acid-corroded sandstone with the soaking time is predicted. Results show that the BI of the sandstone is negatively correlated with the soaking time, and the rate of descent of the BI decreases with the increment of the soaking time. In addition, the decline degree of the BI has a negative correlation with the pH value. On the other hand, the temperature (25°C, 50°C, and 75°C) has greater weakening effects on the BI compared with the impact of the pressure (5 MPa, 10 MPa, and 15 MPa). Besides, days 50 and 120 are two turning points where the decreasing rates of the BI change from rapid to slow and slow to almost constant, respectively. Furthermore, a coefficient (θ) is proposed to quantify the effect of the acid corrosion, and some suggestions are provided for the application of the acidizing treatment.

Experimental Study on the Influence of Freezing Pressure on the Uniaxial Mechanical Properties of Ice

In this study, a test technique that enables continuous control of the sample stress state from freezing to testing is proposed to investigate the influence of freezing pressure on the mechanical properties of ice under uniaxial compression. In this method, the water is frozen into the standard cylindrical ice specimen under high hydraulic pressure in a triaxial pressure chamber, and then, the temperature field and stress field of the ice specimens are adjusted to the initial state of the test; finally, an in situ mechanical test is conducted in the triaxial chamber. The uniaxial compression test of ice specimens with temperature of −20°C and freezing pressure of 0.5–30 MPa is performed in the strain rate range of 5 × 10−5−1.5 × 10−6 s−1. The results show that, as the freezing pressure increases, the ductile-to-brittle transition zone of the ice specimen during failure moves to the low strain rate range, and the failure mode of the specimen changes from shear failure to splitting failure. Further, the brittleness index of the ice specimen first increases, then decreases, and then again increases with the increase in freezing pressure. The brittleness index reaches the maximum (minimum) when the freezing pressure is 30 MPa (20 MPa). The peak stress of the ice specimen also increases first, then decreases, and then increases with the increase in freezing pressure. The maximum value is also at the freezing pressure of 30 MPa, but the minimum value is obtained at the freezing pressure of 0.5 MPa. The failure strain of the ice specimen first decreases and then increases with the increase in freezing pressure, and the maximum (minimum) value is achieved at the freezing pressure of 0.5 MPa (10 MPa). When the ice specimen exhibits brittle failure, the relationships between the residual stress and the freezing pressure and between the peak stress and freezing pressure are the same, but when the ice specimen exhibits ductile failure, there is no obvious relationship between the residual stress and the freezing pressure.