Faculty Publications
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Item Reservoir formation damage during various phases of oil and gas recovery- an overview(2012) Puthalath, P.; Murthy, C.S.N.; Surendranathan, A.O.When a reservoir of oil or gas is discovered under the ground, and reservoir engineers and drilling engineers are employed to tap that reservoir, often, they inadvertently damage it. Formation damage is an undesirable operational and economic problem that can occur during the various phases of oil and gas recovery from subsurface reservoirs including production, drilling, stimulation techniques and work over operations. The formation of a reservoir can be damaged by unforeseen rock, fluid, particle interactions etc and alterations caused by reservoir fluid, flow, and stress conditions. For example, the chemicals that the engineers have injected into the reservoir, the drilling mud used in drilling, or even by stress from the drill bit itself may cause formation damage. Control and remediation of formation damage are among the most important issues to be resolved for efficient exploitation of petroleum reservoirs and cost management. Formation damage seems to be inevitable and whether formation damage can be prevented, removed economically, or must be accepted as the price for drilling and producing a well will depend upon many factors. In this paper a general characteristics of formation damage during various stages of oil exploration are discussed. © 2012 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.Item Prediction of penetration rate and sound level produced during percussive drilling using regression and artificial neural network(2012) Kivade, S.B.; Murthy, C.S.N.; Vardhan, H.The main objective of this investigation is to develop a general prediction model and to study the effect of predictor variables such as uniaxial compressive strength, air pressure and thrust on penetration rate and sound level produced during percussive drilling of rocks. The experiment was carried out using three levels Box-Behnken design with full replication in 15 trials. Modeling was done using artificial neural network (ANN) and multipleregression analysis (MRA). These techniques can be utilized for the prediction of process parameters. Comparison of artificial neural network and multiple linear regression models was made and found that error rate was smaller in ANN than that predicted by MRA in terms of sound level and penetration rate. © 2012 CAFET-INNOVA TECHNICAL SOCIETY.Item Estimating rock properties using sound signal dominant frequencies during diamond core drilling operations(Chinese Academy of Sciences rockgeotech@whrsm.ac.cn, 2019) Vijaya Kumar, C.V.; Vardhan, H.; Murthy, C.S.N.; Karmakar, N.C.In many engineering applications such as mining, geotechnical and petroleum industries, drilling operation is widely used. The drilling operation produces sound by-product, which could be helpful for preliminary estimation of the rock properties. Nevertheless, determination of rock properties is very difficult by the conventional methods in terms of high accuracy, and thus it is expensive and time-consuming. In this context, a new technique was developed based on the estimation of rock properties using dominant frequencies from sound pressure level generated during diamond core drilling operations. First, sound pressure level was recorded and sound signals of these sound frequencies were analyzed using fast Fourier transform (FFT). Rock drilling experiments were performed on five different types of rock samples using computer numerical control (CNC) drilling machine BMV 45 T20. Using simple linear regression analysis, mathematical equations were developed for various rock properties, i.e. uniaxial compressive strength, Brazilian tensile strength, density, and dominant frequencies of sound pressure level. The developed models can be utilized at early stage of design to predict rock properties. © 2019 Institute of Rock and Soil Mechanics, Chinese Academy of SciencesItem Multiple regression model for prediction of rock properties using acoustic frequency during core drilling operations(Taylor and Francis Ltd., 2020) Vijaya Kumar, V.; Vardhan, H.; Murthy, C.S.N.The primary purpose of this study is the quantification of rock properties uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), density and abrasivity, using sound signal dominant frequencies produced during diamond core drilling operations. Rock drilling operations were performed on seven different types of rock samples, using a computer numerical control (CNC) drilling machine. Using the multiple regression analysis, satisfactory mathematical equations were developed for various physico-mechanical rock properties, as well as dominant frequencies of the sound level were generated during diamond core drilling operations. The developed prediction models demonstrated a good regression coefficient between the rock properties and dominant frequencies i.e. the R2 values are 82.50%, 78.41%, 79.40%, and 93.24% for UCS, BTS, density and abrasivity, respectively. The performances indices are: (i) root-mean-square error (RMSE) are 0.102754, 1.241652, 0.396727, and 0.697889 for UCS, BTS, density and abrasivity, respectively; (ii) values account for (VAF) is 82.50008%, 78.41137%, 79.40137%, and 93.23596% for UCS, BTS, density and abrasivity, respectively. Presently, it is in the early stages of development towards the prediction of rock properties using dominant frequencies with the help of audio signal processing in the rock drilling operation. The developed prediction models can be utilised at the early stages of mining and civil engineering projects, for the quantification of rock properties using sound signal dominant frequencies. © 2019 Informa UK Limited, trading as Taylor & Francis Group.Item Artificial neural network for prediction of rock properties using acoustic frequencies recorded during rock drilling operations(Springer Science and Business Media Deutschland GmbH, 2022) Vijaya Kumar, C.V.; Vardhan, H.; Murthy, C.S.N.Determining properties of rocks in rock mechanics/engineering applications such as underground tunnelling, slope stability, foundations, dam design and rock blasting is often difficult due to the requirements of high quality of core rock samples and accurate test apparatus. Prediction of the geomechanical properties of rock material has been an area of interest for rock mechanics for several years now. Nowadays, soft computing methods are used as a powerful tool to estimate the rock properties, cost and duration of the project. This has led to a lack of necessity to develop a model to predict rock properties in the field of rock mechanics. ANN (artificial neural network) models were developed to predict geomechanical properties of the sedimentary rock types using dominant frequencies of an acoustic signal during rock drilling operations. A set of experimental drilling operations test conditions around 875 were used as input parameters including drill bit spindle speeds (rpm), drill bit penetration rates (mm/min), drill bit diameters (mm) and dominant frequencies of the acoustic signal (Hz). The response (output) was uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), density (ρ) and abrasivity (%). The developed models were checked using various performance indices. The results from the analysis show that the suggested ANN model approach is efficient, fits the data and accurately reflects the experimental results. The ANN models predicted physico-mechanical rock properties through the dominant frequency of acoustic signals during rock drilling operations. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature.