Estimated rate of penetration using artificial neural networks and optimize drilling parameters in directional wells in Ahvaz field
Journal Title: Journal of Science and today’s world - Year 2013, Vol 2, Issue 1
Abstract
To further speed up the drilling operations and thus further reducing costs and lower risk operations, we can simulate and predict the conditions to achieve desired results. For this to work with programming and According to data from directional wells drilled in Ahvaz field, can to reached Optimum value for drilling in this field. With The modeling of well conditions and Drilling parameters as for Existing wells in this field, we will find the identical and reliable and functional model. Many factors are effective in rate of penetration. Neural network modeling for the relationship between these variables is very important and many help to optimization the process. In this paper, Using Bourgoyne and Young's equation explains the relationship between these variables. The first step in the application of neural networks made model at the starting drilling point of well. Neural network data can be divided into three parts. 70% of data for network training and 15% of the data for the network Validation and 15% of the data for network sensitivity analysis has been assigned. The percentage error in the calculations must reach down. Because the Studies with the optimal values should be to reduce, the risk and acceleration of drilling process. Cycle process are includes approximately 2-4 million estimate for each analysis. All of these processes are repeated for Establish a relationship between variables and graphs.
Authors and Affiliations
Naser Akhlaghi, atemeh Rezaei, Nima Akhlaghi
Keywords
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