An analytical drilling model of drag bits for evaluation of rock strength

Pre-existing cracks and associated filling materials cause the significant heterogeneity of natural rocks and rock masses. The induced heterogeneity changes the rock properties. This paper targets the gap in the existing literature regarding the adopting of artificial neural network approaches to efficiently and accurately predict the influences of heterogeneity on the strength of 3D-printed rocks at different strain rates. Herein, rock heterogeneity is reflected by different pre-existing crack and filling material configurations, quantitatively defined by the crack number, initial crack orientation with loading axis, crack tip distance, and crack offset distance. The artificial neural network model can be trained, validated, and tested by finite 42 quasi-static and 42 dynamic Brazilian disc experimental tests to establish the relationship between the rock strength and heterogeneous parameters at different strain rates. The artificial neural network architecture, including the hidden layer number and transfer functions, is optimized by the corresponding parametric study. Once trained, the proposed artificial neural network model generates an excellent prediction accuracy for influences of high dimensional heterogeneous parameters and strain rate on rock strength. The sensitivity analysis indicates that strain rate is the most important physical quantity affecting the strength of heterogeneous rock.

Download Full-text

Stress sensitivity of porosity and permeability under varying hydrostatic stress conditions for different carbonate rock types of the geothermal Malm reservoir in Southern Germany

Geothermal Energy ◽

10.1186/s40517-021-00197-w ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Daniel Bohnsack ◽

Martin Potten ◽

Simon Freitag ◽

Florian Einsiedl ◽

Kai Zosseder

Keyword(s):

Effective Stress ◽

Hydraulic Properties ◽

Rock Strength ◽

Pore Network ◽

Stress Sensitivity ◽

Stress Conditions ◽

Rock Matrix ◽

Effective Stresses ◽

Rock Types ◽

Porosity And Permeability

AbstractIn geothermal reservoir systems, changes in pore pressure due to production (depletion), injection or temperature changes result in a displacement of the effective stresses acting on the rock matrix of the aquifer. To compensate for these intrinsic stress changes, the rock matrix is subjected to poroelastic deformation through changes in rock and pore volume. This in turn may induce changes in the effective pore network and thus in the hydraulic properties of the aquifer. Therefore, for the conception of precise reservoir models and for long-term simulations, stress sensitivity of porosity and permeability is required for parametrization. Stress sensitivity was measured in hydrostatic compression tests on 14 samples of rock cores stemming from two boreholes of the Upper Jurassic Malm aquifer of the Bavarian Molasse Basin. To account for the heterogeneity of this carbonate sequence, typical rock and facies types representing the productive zones within the thermal reservoir were used. Prior to hydrostatic investigations, the hydraulic (effective porosity, permeability) and geomechanical (rock strength, dynamic, and static moduli) parameters as well as the microstructure (pore and pore throat size) of each rock sample were studied for thorough sample characterization. Subsequently, the samples were tested in a triaxial test setup with effective stresses of up to 28 MPa (hydrostatic) to simulate in-situ stress conditions for depths up to 2000 m. It was shown that stress sensitivity of the porosity was comparably low, resulting in a relative reduction of 0.7–2.1% at maximum effective stress. In contrast, relative permeability losses were observed in the range of 17.3–56.7% compared to the initial permeability at low effective stresses. Stress sensitivity coefficients for porosity and permeability were derived for characterization of each sample and the different rock types. For the stress sensitivity of porosity, a negative correlation with rock strength and a positive correlation with initial porosity was observed. The stress sensitivity of permeability is probably controlled by more complex processes than that of porosity, where the latter is mainly controlled by the compressibility of the pore space. It may depend more on the compaction of precedented flow paths and the geometry of pores and pore throats controlling the connectivity within the rock matrix. In general, limestone samples showed a higher stress sensitivity than dolomitic limestone or dolostones, because dolomitization of the rock matrix may lead to an increasing stiffness of the rock. Furthermore, the stress sensitivity is related to the history of burial diagenesis, during which changes in the pore network (dissolution, precipitation, and replacement of minerals and cements) as well as compaction and microcrack formation may occur. This study, in addition to improving the quality of input parameters for hydraulic–mechanical modeling, shows that hydraulic properties in flow zones largely characterized by less stiff, porous limestones can deteriorate significantly with increasing effective stress.

Download Full-text

The rock strength in different tension conditions

Journal of Mining Science ◽

10.1007/s10913-009-0071-0 ◽

2009 ◽

Vol 45 (6) ◽

pp. 569-575 ◽

Cited By ~ 23

Author(s):

V. P. Efimov

Keyword(s):

Rock Strength

Download Full-text

Evaluation of PDC bit ROP models and the effect of rock strength on model coefficients

Journal of Natural Gas Science and Engineering ◽

10.1016/j.jngse.2016.08.012 ◽

2016 ◽

Vol 34 ◽

pp. 1225-1236 ◽

Cited By ~ 27

Author(s):

Cesar Soares ◽

Hugh Daigle ◽

Ken Gray

Keyword(s):

Rock Strength ◽

Pdc Bit

Download Full-text

Integration of Chemofacies and Rock Mechanical Properties Using Machine Learning Algorithms: Implications for Geomechanics and Hydraulic Fracture Stimulations in Paleozoic Formations, Saudi Arabia

10.2118/205951-ms ◽

2021 ◽

Author(s):

Maaruf Hussain ◽

Abduljamiu Amao ◽

Khalid Al-Ramadan ◽

Sunday Olatunji ◽

Ardiansyah Negara

Keyword(s):

Machine Learning ◽

Mechanical Properties ◽

Saudi Arabia ◽

Spatial Distribution ◽

Chemical Composition ◽

Rock Strength ◽

Machine Learning Algorithms ◽

Unconventional Reservoirs ◽

Geochemical Properties ◽

Rock Mechanical Properties

Abstract The knowledge of rock mechanical properties is critical to reducing drilling risk and maximizing well and reservoir productivity. Rock chemical composition, their spatial distribution, and porosity significantly influenced these properties. However, low porosity characterized unconventional reservoirs as such, geochemical properties considerably control their mechanical behavior. In this study, we used chemostratigraphy as a correlation tool to separate strata in highly homogenous formations where other traditional stratigraphic methods failed. In addition, we integrated the chemofacies output and reduced Young's modulus to outline predictable associations between facies and mechanical properties. Thus, providing better understanding of lithofacies-controlled changes in rock strength that are useful inputs for geomechanical models and completions stimulations.

Download Full-text