![]() Apparent Rock Strength Logs (ARSL) were generatedĪutomatically by the simulator for the two drilled wells to give an idea of how hard is theįormatiom and the rate of penetration possible for the Bits. The Bit performance optimization potentials. Parameter, survey data, lithology data and Bit information using DROPS simulator to showcase Master's thesis in Petroleum engineeringTwo drilled Wells: Well A and Well B were analysed under the following input data drilling In addition, study shows that by increasing well depth, there is an uncertainty in selecting the jet impact force as the best objective function to determine the effect of Hydraulics on penetration rate. Cumulative probability distribution of predicted ROP shows that the penetration rate can be estimated accurately at 95% confidence interval. ![]() Sensitivity study using analysis of variance shows that well depth, yield point to plastic viscosity ratio, weight on Bit, Bit rotation speed, Bit jet impact force, and 10 min to 10 s gel strength ratio have the greatest effect on ROP variation respectively. Results indicate that the derived statistical model provides an efficient tool for estimation of ROP and determining optimum drilling conditions. Next, bat algorithm (BA) was used to identify optimal range of factors in order to maximize drilling rate of penetration. The important variables include well depth (D), weight on Bit (WOB), Bit rotation speed (N), Bit jet impact force (IF), yield point to plastic viscosity ratio (Y p /PV), 10 min to 10 s gel strength ratio (10MGS/10SGS). Response surface methodology (RSM) was used to develop a mathematical relation between penetration rate and six factors. In this paper, first, the simultaneous effect of six variables on penetration rate using real field drilling data has been investigated. Typical factors include formation properties, mud rheology, weight on Bit, Bit rotation speed, type of Bit, wellbore inclination, and Bit Hydraulics. There are many factors, which determine the drilling rate of penetration. Finally, results indicate that LSSVM is superior over GP in terms of average relative error, average absolute relative error, root mean square error, and the coefficient of determination.Ībstract Rate of penetration (ROP) prediction is crucial for drilling optimization because of its role in minimizing drilling costs. In addition, sensitivity analysis showed that factors of depth, weight on Bit, stand pipe pressure, flow rate and Bit rotation speed account for 93% of total variation of ROP. ![]() Results show that LSSVM estimates 92% of field data with average absolute relative error of less than 6%. Models are a function of depth, weight on Bit, rotation speed, stand pipe pressure, flow rate, mud weight, Bit rotational hours, plastic viscosity, yield point, 10 second gel strength, 10 minute gel strength, and fluid loss. This paper presents two novel methods based on least square support vector machine (LSSVM) and genetic programming (GP). There are various factors influencing ROP such as formation rock, drilling fluid properties, wellbore geometry, type of Bit, Hydraulics, weight on Bit, flow rate and Bit rotation speed. This article describes how the accurate estimation of the rate of penetration (ROP) is essential to minimize drilling costs.
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