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Post-Peak Behaviour Of Sandstones And Effect Of Confining Pressure On Modulus Of Elasticity And Poisson's Ratio
Kumar, Rakesh (GMR Consulting Services Pvt. Ltd) | Sharma, K.G. (Department of Civil Engineering, Indian Institute of Technology Delhi) | Varadarajan, A. (Former Professor, Department of Civil Engineering, Indian Institute of Technology Delhi)
SYNOPSYS: A large number of river valley projects are being developed in the Himalayan region of Indian subcontinent. Most of the rocks show the strain softening in their behaviour. The paper presents the characterization of three types of sandstones viz., Kota sandstone, Dholpur sandstone and Pancheswar sandstone using closed-loop servo-controlled testing machine in the laboratory and their constitutive modeling. The testing on the sandstones is performed using strain controlled loading under unconfined state and confining pressures varying from 5 MPa to 57 MPa. The brittle to ductile transitions is observed for Dholpur and Pancheswar sandstones. The effect of softening is more on fine grained sandstones than that on coarse grained sandstone. The behaviour of sandstones is predicted using Mohr-Coulomb strain softening model available in FLAC. The material parameters for the model are determined from the experimental results. The stress-strain-volume change response of the sandstones is then predicted using the material parameters and is compared with the experimentally observed results. The predicted results and observed responses are generally in good agreement. The effect of confining pressures on modulus of elasticity and Poisson's ratio of sandstones is also studied. The equations between modulus of elasticity and confining pressures and Poisson's ratio and confining pressures are also proposed. The proposed equations are very promising and can be used for field problems. INTRODUCTION Many hydroelectric, transportation, metro rail, nuclear waste repository projects are under construction in the country. The structures for these projects like dams, tunnels, power house caverns are mostly constructed on/in rocks. The rock characterization is the basic input to design these structures. Many rocks generally show strain softening behaviour under loading. Therefore it is very important to incorporate the strain softening behaviour for the analysis and design of these structures. Strain softening is defined as the progressive loss of strength when material is compressed beyond peak [1]. The strain softening has significant impact on tunnel behaviour. Very few researchers have considered the effect of strain softening of rocks on tunnels [2,3,4]. The paper presents the characterization of three types of sandstones viz., Kota sandstone, Dholpur sandstone and Pancheswar sandstone using closed-loop servo-controlled testing machine in the laboratory and their constitutive modelling. EXPERIMENTAL INVESTIGATION The paper presents the characterization of three types of sandstones viz., Kota sandstone, Dholpur sandstone and Pancheswar sandstone using closed-loop servo-controlled testing machine in the laboratory and their constitutive modeling. Kota sandstone is from Rajasthan belonging to Bhander series of upper Vindhyan and is medium to fine grained with reddish colour with small grey spots. Dholpur sandstone is Pinkish in colour and fine grained and is from Rajasthan. The machine used for testing has a loading capacity of 1000 kN and has loading rate capability in the range of 0.001mm/s to 10 mm/s. The stiffness of the machine is 1700 kN/mm. High pressure triaxial cell which has the capability of applying 0–140 MPa confining pressure and has sufficient space to accommodate extensometers inside, is used for the testing.
SYNOPSIS: This paper presents the development of Artificial Neural Network (ANN) models for predicting the stress-strain response of intact and jointed rocks. The stress-strain behaviour of ten different types of intact rocks is predicted by specifying the uniaxial compressive strength of the intact rocks, confining pressure and axial strain as inputs. The stress-strain response of jointed rocks is predicted by specifying the intact rock properties, confining pressure, joint properties and axial strain as inputs. The database used in this paper for the ANN analysis consists of five types of jointed rocks namely Plaster of Paris, block jointed Gypsum Plaster, Jamrani Sandstone, Agra Sandstone and Granite tested in triaxial compression under a wide range of confining pressures at different joint orientations and joint frequencies. The results obtained from the ANN analysis are compared with the experimental measurements and the results obtained from the continuum approach. Results from the analyses showed that the neural network approach is effective in capturing the stress-strain behaviour of intact rocks and the complex stress-strain behaviour of jointed rocks. INTRODUCTION Jointed rock mass is a natural material with a large variation in its properties (highly heterogeneous and anisotropic). The prediction and estimation of its properties involve many uncertainties, as the material behaviour is stress path dependent. Also, obtaining an undisturbed sample is very difficult in jointed rock mass. Particularly, developing a general model which incorporates many rock types and includes intact and jointed rocks considering all the parameters (confining pressure, strength properties and joint properties) which govern the engineering behaviour of these rocks in an attempt to predict their stress-strain response is next to impossible. No such model exists in the literature and all the available models are only highly empirical ones. Artificial Neural Networks have been found to be very efficient and intelligent in handling non-linear relationships among the parameters involved in numerical modelling. Unlike the standard computational methods, neural networks use a parallel approach analogous to the functioning of the human brain. Application of neural networks for problems in rock mechanics was explored by several researchers earlier (Feng et al., [1]; Meulenkamp and Grima, [2]; Sitharam et al., [3]; Sonmez et al., [4]; Arunakumari and Latha, [5]). In this paper, an attempt has been made to construct Artificial Neural Network (ANN) models for predicting the stress-strain response of intact and jointed rocks. The objective of this paper is to develop a generalized network architecture which can predict the stress-strain response of any type of jointed rocks given the intact rock properties, confining pressure under which the rock is tested and the joint properties. The present paper covers two parts. In the first part the stress-strain behaviour of ten different types of intact rocks is predicted by specifying the uniaxial compressive strength of the intact rocks, confining pressure and axial strain as inputs. In the second part, the stress-strain response of jointed rocks is predicted by specifying the intact rock properties, confining pressure, joint properties and axial strain as inputs.
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.52)
- Energy > Oil & Gas (0.69)
- Materials > Construction Materials (0.58)
Abstract: A good understanding of the subsurface distribution of rock properties and its uncertainties are important elements in the development of safe and economical geo-engineering projects. The aim of this paper is to describe the steps to be followed in order to construct a comprehensive model of the rock mass. A brief review of the main elements of 3D geological and geomechanical modeling is presented followed by a discussion of the main tools available for applications to rock engineering. One case history that describes the evaluation of rock mass conditions for a powerhouse excavation in a metamorphic environment is presented. Geostatistical techniques were used to construct a model of rock fracturing and rock mass strength throughout the building area. After the excavation the rock model was compared with conditions displayed by the actual rock mass. 1. INTRODUCTION The study and understanding of a complex environment of difficult access, the subsurface, has always been a challenge and the objective of geoscientists and some related fields, like geotechnical, mining and petroleum engineering. Engineering works are designed and investments are secured based primarily upon site characterization. Therefore, good site characterization, with distribution of rock properties and its associated uncertainty, is of fundamental importance for the decision process and management of geo-engineering projects. Traditional geotechnical engineering works are usually based on 2D representations of data between boreholes (the so-called geotechnical cross-sections). These cross-sections are not able to fully represent the global distribution of properties on the subsurface though carried out by highly skillful geologists. Industries like oiland- gas and mining, on the other hand, use sophisticated 3D modeling techniques to predict subsurface property distribution and its associated uncertainties, a process called 3D geological characterization [1]. The geological characterization comprises actions for the acquisition and interpretation of data from different sources and scales combined with a 3D representation and hierarchical importance for developing a model. The modeling procedure was first used in the mining industry and later was adapted and improved by the oil-and-gas industry, which introduced the concept of Common Earth Model or Shared Earth Model. The construction of these models depends very much upon the existence of cross-disciplinary teams in order to unite the knowledge of different areas [2]. Following the examples of these industries, the geotechnical community has made some efforts to change the traditional practice. Recently, many studies that employ this technique to develop 3D models for site characterization have been published [3–7]. This process, however, has evolved slowly, because civil engineers argue that the costs to develop these models are too high [8]. An explanation about geological and geomechanical elements involved in the model build up process is presented and one case history is described to illustrate the proposed workflow. 2. METHODOLOGY The first step during the development of a 3D earth model is to define the final objective, i.e., what are the models for? Who are they for? [9]. Once the final objective of the model has been defined, data requirements and their collection procedures can be established.
- Europe (0.46)
- South America > Brazil (0.30)
- North America > Canada (0.28)
- Asia > India (0.28)
Synopsis: Major factors influencing critical well bore pressures are summarized and used in a study in which well collapse pressure is calculated using the Mohr-Coulomb, the Modified Lade, and two versions of the Drucker-Prager criterion. Both linear and nonlinear rock models are considered. Compared to the other two failure criteria, the Inscribed Drucker-Prager and the Mohr-Coulomb are conservative as they predict higher well collapse pressure than the other two criteria, i.e. the Modified Lade and the Circumscribed Drucker- Prager. On the other hand, the latter provides very optimistic predictions that are significantly below predictions given by the other three criteria. Thus, the outer Drucker-Prager seems to be significantly underestimating well collapse pressure. In addition, the collapse pressure obtained using the modified Lade criterion is lower than the corresponding pressure obtained from the Mohr-Coulomb criterion, which – in turn – is below the critical well pressure obtained from the Inscribed Drucker-Prager criterion. Linear elastic collapse pressures are above the corresponding critical well pressures obtained when nonlinear material properties are taken into account. The effect of wellbore inclination on stability is also discussed. 1. INTRODUCTION Critical well bore pressures are commonly used in petroleum engineering design and monitoring of drilling and production operations to minimize the risk of excessive costs incurred as a result of wellbore instability incidents. Wellbore instabilities receive special attention due to their huge economic impact. The related downtime associated with well construction is about 10–15% of total well cost or about 50% of total nonproductive time [1]. Among many factors that typically enter wellbore stability studies such as in-situ state of stress, pore pressure regime, rock mechanical and constitutive properties, the wellbore direction and inclination, and – sometimes – thermal effects and chemical factors, the accuracy of critical wellbore pressure predictions will depend strongly on the correctness of the failure criterion used and the possibilities are many. Well drilling disturbs the natural stress state in the rock, causes stress redistributions and produces stress concentrations at or near the borehole wall. This may potentially lead to different types of hole problems such as stuck pipe, borehole collapse, etc. Mechanical wellbore stability is discussed in terms of critical wellbore pressures that are related to rock stresses around a wellbore: sz (axial), sr (radial), and sq (hoop), Fig. 1. It is defined by specifying critical well pressures or the mud weight window, i.e. the lower and upper limit of drilling mud weight that can be used for safe drilling of a new well. The lower limit is associated with shear failure (borehole breakouts, Fig. 1, are also evidence of shear failure) and calculated using the rock failure criteria. The upper limit corresponds to rock fracturing when tensile rock failure occurs, i.e. the tangential stress at the wellbore wall exceeds the tensile strength To of the rock, sq >To. Well fracturing pressure is calculated from this criterion. Exceeding the upper wellbore pressure may lead to lost circulation but sometimes is done intentionally as in well fracturing for stimulation or in-situ stress measurements [2].
- Europe (0.68)
- North America > United States (0.46)
- Asia > India (0.46)
- Asia > Middle East > UAE (0.28)
Predicting Penetration Rate Of A Tunnel Boring Machine Using Artificial Neural Network
Eftekhari, M. (Mining Engineering Department, Isfahan University of Technology (IUT)) | Baghbanan, A. (Mining Engineering Department, Isfahan University of Technology (IUT)) | Bayati, M. (Imen Sazan Consulting Ins., Tehran)
ABSTRACT: In tunneling a reliable prediction of advance rates is essential for calculating budget and construction time. Estimation of penetration rate is expressed as the basis for predicting advance rate in underground excavation using Tunnel Boring Machine. In this study the obtained data from 10KM of excavated Zagros tunnel project in Iran were subjected to statistical analyses using MATLAB for ANN modeling. When the neural network has been successfully trained, its performance is tested on a separate testing data set. Finally, the penetration rate was predicted by the trained neural network. The results show that the developed ANN method is efficient for predicting the PR in Zagros tunnel. The ANN model for next 0.5 KM which is recently excavated is well compatible with the real calculated PR of TBM in the field with 79% confident level. Result of sensibility analysis on the effect of Thrust and Torque on the PR shows that the maximum PR in given ground condition occurred in the optimum limits of Thrust and Torque. 1 INTRODUCTION Since the first Tunnel Boring Machine (TBM) was built, the performance analysis and the development of accurate prediction models of the machines have been the ultimate goals of many research works [1–9]. A reliable prediction of TBM performance is needed for time planning and budget control of projects. Both penetration rate (PR) and advance rate (AR) are estimated in performance prediction of TBM. Penetration rate is defined as the distance excavated divided by the operating time during a continuous excavation phase, while advance rate is the actual distance mined and supported divided by the total time [10]. The AR includes downtimes for TBM maintenance, machine breakdown and tunnel failure. Even in stable rock, the rate of advance is considerably lower than the net rate of penetration and utilization coefficients (U%=(AR/PR)×100) is estimated about 30–50% mainly due to a TBM daily maintenance. When low quality rocks are excavated, the penetration rate could potentially be very high. However it demands a strong support pattern and face to rock jams and gripper bearing failure which results a relatively low advance rate with utilization coefficients about 5- 10% [11]. According to the literature, important parameters in TBM performance could be categorized in two major parts as follows:Ground Condition: It includes characteristic parameters of intact rock and rock mass properties. Mostly reported important properties of intact rock in TBM performance are Uniaxial Compressive Strength (UCS), Brazilian Tensile Strength (BTS) and Point load index (Is50). In terms of Rock Mass properties, discontinuity spacing (Js), Rock Quality Designation (RQD), the angle between the tunnel axis and the planes of weakness (Discontinuity Dip and Dip Direction), rock mass quality using classification system such as RMR, Q and GSI have significant impact on TBM performance. TBM operational parameters such as value of thrust, torque, Round Per Minute (RPM) and disc specifications have great influence on TBM performance. Tarkoy (1973) presented a model to predict penetration rate of TBM using only total hardness of intact rocks.
Abstract: Exploitation of minerals is a major source of revenue stimulating growth of a nation. Wave of globalization has further augmented endeavors in this direction leading to extraction of ore locked in remnant mine pillars situated in high ground stresses. One mega operation was carried out at Mochia mine involving mass firing of 0.55 million tonne of ore locked in a major mine pillar structure. Economic and safe extraction of minerals required not only the prediction of ground conditions through numerical simulations but taking preventive measure proactively also. Accordingly, whole of the depillaring operation was simulated on a Displacement Discontinuity based Software "N-Fold". To predict extent of failure of the critical mine openings a model (known as Drive Failure Model- correlating extent of failure with stress level) formulated by the authors was applied. Based on the outcome, mine openings were pre-supported prior to the mass firing the pillars. After the mass blast, these locations were again examined to determine the extent of failure and their safety status. The post blast observations revealed that the predictions made regarding extent of failure by the DFM are very encouraging. Success of the instant predictive modelling highlights potential of well calibrated numerical modelling carrying out safe and economic extraction of minerals at ever increasing depth. 1. INTRODUCTION Mochia, a Lead Zinc Underground mine of Hindustan Zinc Ltd., India, deploys open stoping for extraction of Lead Zinc ore. With progress of mining down the depth, a battery of mine pillars are formed. These mine pillars in due course of time started deteriorating causing unsafe and unpredictable ground conditions in the mine. In order to continue safe and economic extraction down the depth, some of these pillars needed their extraction (Rajmeny, unpubl. 2000). A mega operation involving mass mining of mine pillars (de-pillaring) having 0.55million tones of ore was carried out at Mochia. Safe re-entry into the mine for continuance ore extraction required numerical simulation of the depillaring and prediction of ground conditions of some of the strategic mine openings like haulage drive, extraction level, etc. Accordingly, whole of the depillaring operation was simulated on a Displacement Discontinuity based Software "N-Fold" (Golder, 1990). The Drive Failure model (DFM) – an empirical model- developed by the authors for predicting failure in drives (Rajmeny et al. 2002; Rajmeny et al. 2004) was applied to predict behaviour of the mine openings during the mass blasting at Mochia mine. It was also required to predict the behaviour of crown and rib pillars. On basis of the predictions, preventive measures including pre-supporting of the major areas of the mine such as main haulage drive at 39mRL (9th level) and LHD Garage were undertaken. These proactive measures manifested in safer re-entry in mine and resumption of ore extraction from the aforesaid pillar mass firing. Post blast ground observations were carried out to validate the DFM failure predicting model by comparing the predicted behaviour of different openings of the mine with the actual ground conditions.
Practices To Control Rock Burst In Deep Coal Mines Of Upper Silesian Coal Basin And Their Applicability For Disergarh Seam Of Raniganj Coalfield
Konicek, Petr (Institute of Geonics Academy of Sciences) | Soucek, Kamil (Institute of Geonics Academy of Sciences) | Stas, Lubomir (Institute of Geonics Academy of Sciences) | Singh, Rajendra (Central Institute of Mining & Fuel Research (CIMFR, under CSIR)) | Sinha, Amalendu (Central Institute of Mining & Fuel Research (CIMFR, under CSIR))
Abstract: This paper presents geo-mining conditions along with the problems of coal bumps encountered at Chinakuri Mine of ECL (Raniganj coalfield) and Lazy Mine of OKC. Further, the developed and practiced measures at Lazy Mine of OKC to control the coal bump are detailed and analysed to assess their suitability for underground extraction of Dishergarh coal seam of Chinakuri Mine, ECL. It is observed that the conventional techniques, being practiced to release the stress concentrations and to create a network of fissures in the solid accompanying rocks in Indian coalfield, need a complete change. On the basis of this analysis, a suitable method of mining with overlying strata management approach is advised to suit the conditions of the Chinakuri Mine of ECL. 1. INTRODUCTION Out of different rock mechanics problems of underground coal mining of deep seated deposits, coal bump/rock burst is identified (CMRI, 1994) as a major hazard during underground coal mining at greater depth. Coal bump/rock burst engage violent and rapid failure of coal/rock in and around an underground excavation. Sudden release of accumulated elastic stain energy from a rock mass in the free face, created due to excavation, is the origin of this phenomenon and is, mainly, related with the geo-mining conditions of the site, characteristics of the coal/rock mass and stress regime of the area. CIMFR undertook an investigation related to this issue (CMRI, 1994) but this investigation remained limited, mainly, to identify different coal seams of the country likely to pose the coal bump/rock burst problems and their causative factors. Some approaches to control the problems of coal bump/rock bursts were also investigated but their field application achieved partial success. However, this study could project characteristics (Table –1) of some the coal seams and found that the Dishergarh coal seam of Chinakuri Mine (Raniganj Coalfield) is one of the most bump/burst susceptible seam in the country. Recently, CIMFR has collaborated with the Institute of Geonics, Ostrava, the Czech Republic for rock mechanics investigations to meet challenges of strata control of deep underground coal mining. During this collaboration, the success of the Czech counterpart in controlling coal bumps/rock bursts during underground visits of Czech mines is experienced. This paper describes the geo-mining conditions of Chinakuri Mine of ECL and the results of investigations taken to characterise the coal/rock mass of the mine. 2. Chinakuri Mine Chinakuri Colliery 1&2 Pits of ECL is situated in the heart of the Raniganj coalfield on the bank of river Damodar near Asansol city of West Bengal. This is the deepest coal mine in the country, where underground mining is taking place at nearly 700 m depth of cover. Before nationalisation, this mine was owned by M/S. Andrew Yule & Co. and has experienced mining of a number of coal seams by different techniques. However, mining of the Dishergarh coal seam at this mine by bord and pillar and longwall methods of this colliery has always been a problem, mainly due to occurrence of coal bumps.
- Europe (1.00)
- Asia > India > West Bengal > Burdwan (1.00)
An Artificial Neural Network Model To Predict The Performance Of Hard Rock TBM
Hedayatzadeh, M. (Islamic Azad University) | Shahriar, Kourosh (Department of Mining and Metallurgical Engineering, Amirkabir University of Technology) | Hamidi, Jafar Khademi (Department of Mining and Metallurgical Engineering, Amirkabir University of Technology)
1. INTRODUCTION Performance prediction of tunnel boring machine is one of the geotechnical problems that commonly have complexity and ambiguity. Over the last few years, many researchers have made attempt to set up accurate models for predicting TBM performance prediction. This issue is crucial because a precise estimation of machine performance can considerably decrease the capital costs of mechanical excavation project. Performance prediction of TBM strictly relies on the estimation of the rate of penetration (ROP), defined as the ratio of excavated distance to the operating time during continuous excavation phase, and advance rate (AR), the ratio of both mined and supported actual distance to the total time. Many attempts were made for the development of the accurate prediction models [1–10]. In addition to these models in recent years some prediction models have been developed using artificial intelligences including artificial neural network (ANN), fuzzy logic and neuro-fuzzy [11–23]. Taking into consideration the nature of problem, the main purpose of the present study is to develop a model by utilizing the ANN for predicting TBM performance. In order to achieve this aim, a database composed of rock mass properties such as fabric indices of four rock mass classification and the angle between plane of weakness and tunnel axis, intact rock properties including uniaxial compressive strength, machine specification including net thrust per cutter together with actual measured TBM penetration rate, was compiled along the 6.5 km bored Alborz service tunnel. 2. PROJECT DESCRIPTION AND GEOLOGY OF THE STUDY AREA Alborz service tunnel is the longest tunnel section (6.5 km) along Tehran-Shomal Freeway, situated in the high elevation portions of Alborz Mountain Range, connecting the capital city of Tehran to the Caspian Sea in the North (Fig.1). The service tunnel with diameter of 5.20 m was excavated by an open gripper TBM in advance of two main tunnel tubes to be excavated subsequently. The purpose of the service tunnel is site investigation, drainage of the rock mass, providing access for main tunnel excavations and service, ventilation and drainage during operation of the complete tunnel system. 3. PARAMETERS INFLUENCING THE PERFORMANCE PREDICTION OF TBM Prediction of TBM penetration rate is the most crucial issue to estimate machine performance and contains a large number of influencing parameters, in general, four main categories including rock material and rock mass parameters, machine characteristics and operational parameters given as follows: 3.1. Intact rock characteristics Intact rock strength (e.g. uniaxial compressive strength "UCS", Brazilian tensile strength "BTS", Point load index "Is (50)") Toughness (Punch penetration index, Fracture toughness index) Hardness and drillability (Siever's J-value, Total & Taber hardness index, Schmidt hammer hardness) Brittleness (Swedish brittleness number "S20", brittleness indices; B1= σc / σt and B2= [(σc – σt) / (σc + σt)]), where σc and σt are uniaxial compressive and tensile strength of intact rock, respectively Abrasiveness indices (Cerchar Abrasivity Index "CAI", Abrasion Value "AV") Others (Poisson ratio "ν", Elasticity modulus "E", Internal friction angle "φ", Porosity, Grain size etc.)
- North America > United States (1.00)
- Asia > Middle East > Turkey (0.46)
- Asia > Middle East > Iran > Tehran > Tehran (0.46)
Estimation Of Penetration Rate Of Rotary Drills Using A New Drillability Index In Sarcheshmeh Copper Mine
Cheniany, Alireza (Department of Mining and Metallurgical Engineering, Amirkabir University of Technology) | Khoshrou, Seyed Hasan (Department of Mining and Metallurgical Engineering, Amirkabir University of Technology) | Shahriar, Kourosh (Department of Mining and Metallurgical Engineering, Amirkabir University of Technology) | Hamidi, Jafar Khademi (Department of Mining and Metallurgical Engineering, Amirkabir University of Technology)
Abstract: The performance of drilling process is significantly influenced by intact rock and rock mass parameters. However, taking into account all these parameters for estimation of rock drillability is not an easy task. The main purpose of this study is to provide a practical convenient model based on rock mass characteristics and geological sampling from blast holes and operational factors. For this purpose, an empirical formula was developed using linear multiple regression to predict penetration rate in Sarcheshmeh copper mine in Iran. Seven parameters of rock material and rock mass including uniaxial compressive strength (UCS) of rock material, Schmidt hammer hardness value, quartz content, fragment size (d80), alteration, joint dipping as well as two operational parameters of rotary drill including bit rotational speed and thrust were taken into consideration for estimation of newly developed Specific Rock Mass Drillability (SRMD) index. Multiple linear regression analyses were used to develop a new equation for predicting the penetration rate of rotary drills. Comparison of measured SRMD with those predicted by multi-linear regression model showed good agreement with correlation coefficients of 0.82. This highlights the potential of multivariate regression model of rock mass characteristics in rotary drill performance prediction. However, the relationships obtained in this analysis should be considered valid only for geological settings similar to those of Sarcheshmeh copper mine. 1. Introduction An accurate prediction of blast hole drilling rate helps to make more efficient the planning of drilling operations in a mine [1]. The drillability of rocks depends on many factors, in brief controllable and uncontrollable parameters. Bit type and diameter, rotational speed, thrust, blow frequency and flushing are the controllable parameters. On the other hand the parameters such as rock properties and geological conditions are the uncontrollable parameters. Many investigators have been tried to correlate drillability and various mechanical rock properties. Pfleider and Blake [2] concluded that a rough correlation exists between penetration rate and size range of cuttings, i.e. the higher the penetration rate, the coarser the particle size. Maurer [3] studied crater formation under an indenter and identified three distinct phases for the brittle rocks including crushing of surface irregularities and elastic deformation, extension of crushing zone beneath an indenter, formation of chips. Fish [4] developed a model for rotary drills with penetration rate directly proportional with thrust and inversely proportional with uniaxial compressive strength. Singh [5] showed that compressive strength is not directly related to the drilling rate of a drag bit. Selim and Bruce [6] developed a penetration rate model for percussive drilling using stepwise linear regression analysis. The model is a function of the drill power and the physical properties of the rocks penetrated. Clark [7] stated that drilling strength is mainly dependent on hardness and triaxial strength of rock. Rabia [8] determined surface area and rock impact hardness number from the results of percussive drill cuttings that failed to give correlation with the drill variables. Howart and Rowland [9] correlated rock texture with rock strength and drillability.
- Asia > Middle East > Iran (0.35)
- Asia > India (0.28)
- Research Report > New Finding (0.71)
- Research Report > Experimental Study (0.56)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Mineral > Native Element Mineral > Copper (0.84)
- Well Drilling > Wellbore Design > Rock properties (1.00)
- Well Drilling > Drilling Operations (1.00)
- Well Drilling > Drill Bits (1.00)
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