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ABSTRACT: In this study, an artificial neural networks (ANN) model as an artificial intelligence (AI) technique is proposed to determine the formation pore pressure from data of two critical drilling parameters named mechanical specific energy and drilling efficiency. These parameters (MSE and DE) which are closely correlated to differential pressure during drilling were chosen as a result of a literature review of proposed methods of pore pressure estimation. Collected data of a three wellbores drilled in an Iranian sandstone formation were used for the purpose of this research, and pore pressure estimated using this model was in a good agreement with estimates from previously published models including the one derived from conventional sonic logs data. The proposed model results were analyzed, and proved that artificial neural networks are capable to provide reliable independent predictions of pore pressure, and this smart model can be hired to analyze data for pre-drilling prediction models construction and post-well prediction models optimization.
Nowadays, many drilling operations are not being performed with optimum efficiency and management of costs, time and quality. Thus, drilling optimization has become an important and critical challenge for drilling operators in the petroleum industry as there are a lot of variables for consideration in drilling systems optimization. Real-time analysis of drilling parameters’ data is a way to understand drilling mechanics and efficiency. (Amadi and Iyalla, 2012)
Estimates of Formation Pore Pressure before and while drilling, and recognizing deviations from the expected pressure are important inputs for well planning and operational decision making. The effect of Differential Pressure (wellbore pressure minus pore pressure) on drilling responses has long been recognized, and drilling efficiency (DE) and mechanical specific energy (MSE) are chosen as parameters highly correlated to the differential pressure. (Majidi et al. 2016)
Logs and drilling mechanics based estimation methods are independent models of pore pressure estimation when suitable data are available. However, the advantage of drilling mechanics method is that it can provide pressure while drilling in real-time at the bit, not behind the bit, and the error would be diminished.
Tariq, Zeeshan (KFUPM) | Elkatatny, S. M. (KFUPM) | Mahmoud, M. A. (KFUPM) | Abdulraheem, A. (KFUPM) | Abdelwahab, A. Z. (KFUPM) | Woldeamanuel, M. (Saudi Aramco) | Mohamed, I. M. (Advantek Waste Management Services)
ABSTRACT: Unconfined compressive strength (UCS) is the key parameter to; estimate the in situ stresses of the rock, alleviate drilling problems, design optimal fracture geometry and to predict optimum mud weight. Retrieving reservoir rock samples throughout the depth of the reservoir section and performing laboratory tests on them are extremely expensive as well as time consuming. Therefore, mostly UCS predicted from empirical correlations. Most of the empirical correlations for UCS prediction are based on elastic parameters or on compressional wave velocity. These correlations were developed using linear or non-linear regression techniques. This paper presents a rigorous empirical correlation based on the weights and biases of Artificial Neural Network to predict UCS. The testing of new correlation on real field data gave a less error between actual and predicted data, suggesting that the proposed correlation is very robust and accurate. Therefore, the developed correlation can serve as handy tool to help geo-mechanical engineers in order to determine the UCS.
UCS defines the strength of the rock when subjected to uniaxial loading. Under unconfined conditions, it is the maximum axial compressive stress that a perfectly right-cylindrical sample of material can withstand. UCS has been used widely in petroleum industry to estimate the in-situ stresses of the rock, to make geo-mechanical earth model, to optimize of rate of penetration in drilling and to design optimal hydraulic fracture parameters in production (Gatens et al., 1990). Accurate prediction of UCS can avoid severe drilling problems which includes well bore collapse, hole pack off, sand production and tight holes (Khaksar et al., 2009).
UCS can be determined by means of static methods. These methods are uniaxial and tri-axial compressional tests, which are conducted on a right cylindrical cores retrieved from the depth of the interest. These tests measure the deformation of rock sample by the application of known force (Barree et al., 2009). The stress-strain deformation curves are generated from these tests. These curves are traced and analyzed to obtain UCS as well as elastic parameters (Jaegar et al., 2007).