The Experts below are selected from a list of 159 Experts worldwide ranked by ideXlab platform
Jefferson W. Tester - One of the best experts on this subject based on the ideXlab platform.
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uncertainty analysis of geothermal well Drilling and completion Costs
Geothermics, 2016Co-Authors: Maciej Z Lukawski, Rachel L Silverman, Jefferson W. TesterAbstract:Abstract The goal of this study was to characterize the uncertainty associated with the Cost of Drilling and completion of geothermal wells. Previous research and publications have produced correlations for the average Cost of geothermal wells as a function of well depth. This project develops this concept further by using a probabilistic approach to evaluate the distribution of geothermal well Costs for a range of well depths. The well Cost uncertainty was characterized by identifying the main Cost components of geothermal wells and quantifying the probability distributions of the key variables controlling these Costs. These probability distributions were determined based on the detailed Cost records of U.S. geothermal wells drilled or designed from 2009 to 2013 as well as Cost data from Drilling equipment manufacturers and vendors. Probability distributions of the key variables were examined to find statistically significant correlations between them. Lastly, the previously determined probability distributions of individual Cost components and the correlations between them were input into WellCost Lite, a predictive geothermal Drilling Cost model, using the Monte Carlo method. This approach allowed us to generate the overall well Cost probability distributions for 8000–15,000 ft. (2400–4600 m) geothermal wells. We have shown that the median geothermal well Cost increases exponentially with depth. Deep wells typically have higher Cost uncertainty and more positively-skewed Cost probability distributions. The correlations presented in this paper can be used to determine the economic feasibility of geothermal energy systems, assess the project risk, and facilitate investment decisions.
Yunhu Lu - One of the best experts on this subject based on the ideXlab platform.
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theoretical and experimental study on the penetration rate for roller cone bits based on the rock dynamic strength and Drilling parameters
Journal of Natural Gas Science and Engineering, 2016Co-Authors: Yong Deng, Mian Chen, Yakun Zhang, Yunhu LuAbstract:Abstract Roller cone bits have been used extensively in petroleum and natural gas mining, and the accurate prediction of their rate of penetration (ROP) is of crucial importance for improving Drilling quality and reducing Drilling Cost. In this study, a new prediction model of the ROP considering the combined effect of the main Drilling parameters and rock dynamic compressive strength is developed for roller cone bits. The model is derived based on the mechanism of rock fragmentation under a single tooth impact indentation. The newly introduced ROP model is different from others in that it replaces the rock static strength with rock dynamic compressive strength and can reflect the real process of rock dynamic crushing by a roller cone bit. The results of theoretical analysis show that the ROP linearly correlates with the bit rotary speed; the relationship between the ROP and weight on bit (WOB) is 3/2 power, and the relationship between the ROP and rock dynamic compressive strength is −3/2 power. Furthermore, lab Drilling tests on sandstone and limestone are carried out with a full-scale bit Drilling testing machine, and the influence laws of WOB, rotary speed and rock dynamic strength on the ROP are analyzed. In addition, the theoretical ROP values calculated based on the rock dynamic compressive strength and static compressive strength are compared with the experimental ROP values. The results from the tests indicate that the experimental ROP values are basically consistent with the theoretically derived values, and the comparative results show that the theoretical ROP values calculated according to the rock dynamic compressive strength are closer to the actual values with, an average relative error below 15%, signifying that the newly established ROP model in this paper is valid and can be used to predict the Drilling rate with reasonable accuracy and can be used to provide useful guidance for optimizing Drilling parameters.
Ma Gang - One of the best experts on this subject based on the ideXlab platform.
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forecast of oil gas Drilling Cost based on bp neural network
Journal of Xi'an Shiyou University, 2010Co-Authors: Ma GangAbstract:Oil-gas Drilling Cost is an important index reflecting the economic benefit of an oilfield enterprise.The accurate forecast of the Drilling Cost can help enterprise directors and investors to carry out scientific decision and estimation.Based on analyzing the influential factors of oil-gas Drilling Cost,a Drilling Cost forecasting model is established using BP neural network,in which the relationship among the factors is considered.Taking the Drilling work data of some oilfield as an example,it is proven that the method has higher forecast precision than linear regression method and BP neural network method.
Bernt S Aadnoy - One of the best experts on this subject based on the ideXlab platform.
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real time software to estimate friction coefficient and downhole weight on bit during Drilling of horizontal wells
ASME 2014 33rd International Conference on Ocean Offshore and Arctic Engineering, 2014Co-Authors: Mazeda Tahmeen, Geir Hareland, Bernt S AadnoyAbstract:The increasing complexity and higher Drilling Cost of horizontal wells demand extensive research on software development for the analysis of Drilling data in real-time. In extended reach Drilling, the downhole weight on bit (WOB) differs from the surface seen WOB (obtained from on an off bottom hookload difference reading) due to the friction caused by drill string movement and rotation in the wellbore. The torque and drag analysis module of a user-friendly real-time software, Intelligent Drilling Advisory system (IDAs) can estimate friction coefficient and the effective downhole WOB while Drilling. IDAs uses a 3-dimensional wellbore friction model for the analysis. Based on this model the forces applied on a drill string element are buoyed weight, axial tension, friction force and normal force perpendicular to the contact surface of the wellbore. The industry standard protocol, WITSML (Wellsite Information Transfer Standard Markup Language) is used to conduct transfer of Drilling data between IDAs and the onsite or remote WITSML Drilling data server.IDAs retrieves real-time Drilling data such as surface hookload, pump pressure, rotary RPM and surface WOB from the data servers. The survey data measurement for azimuth and inclination versus depth along with the retrieved Drilling data, are used to do the analysis in different Drilling modes, such as lowering or tripping in and Drilling. For extensive analysis the software can investigate the sensitivity of friction coefficient and downhole WOB on user-defined drill string element lengths. The torque and drag analysis module, as well as the real-time software, IDAs has been successfully tested and verified with field data from horizontal wells drilled in Western Canada. In the lowering mode of Drilling process, the software estimates the overall friction coefficient when the drill bit is off bottom. The downhole WOB estimated by the software is less than the surface measurement that the drillers used during Drilling. The study revealed verification of the software by comparing the estimated downhole WOB with the downhole WOB recorded using a downhole measuring tool.Copyright © 2014 by ASME
Liu Tianshi - One of the best experts on this subject based on the ideXlab platform.
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bayesian regularization bp neural network model for predicting oil gas Drilling Cost
BioMedical Engineering and Informatics, 2011Co-Authors: Zhao Yue, Zhao Songzheng, Liu TianshiAbstract:Oil-gas Drilling Cost is an important indicator which reflects the economic benefit of oilfield enterprise. Following taking the characteristics of oil-gas Drilling Cost which belongs to subsidiary of CNPC (China National Petroleum Corporation) into account, determinants concerning oil-gas Drilling Cost are identified. Bayesian Regularization Back Propagation Neural Network (BRBPNN) is proposed to predict oil-gas Drilling Cost. Through comparing with Levenberg-Marquardt Back Propagation, Momentum Back Propagation, Variable Learning Rate Back Propagation models in terms of prediction precision, convergence rate and generalization ability, the results exhibit that BRBPNN has better comprehensive performances. Meanwhile, results also exhibit that BRBP model has the automated regularization parameter selection capability and may ensure the excellent adaptability and robustness. Thus, this study lays the foundation for the application of BRBPNN in the analysis of oil-gas Drilling Cost prediction.
