Effect of Organic Ash on Rheological Properties on Drilling Fluid

  • Sneha H Department of Petroleum Engineering, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India.
  • Subha M Department of Petroleum Engineering, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India
  • A. Nagarajan Assistant Professor, Department of Petroleum Engineering, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India
  • K. Bogeswaran Assistant Professor, Department of Petroleum Engineering, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India
Keywords: Drilling Mud, Organic Ash, Rheological Properties, Barite, well condition

Abstract

Successful oil and gas extraction is the end goal of any drilling operation, which involves planning, drilling, evaluating, and finishing a well. Since drilling fluids serve so many purposes in making this possible, it is critical that they be carefully designed. An experiment was conducted in the lab for this investigation. To improve the qualities of drilling mud and lessen the harmful effect of organic ash on the environment and human health, this study examines the behaviour and properties of drilling mud with the addition of different concentrations by weight of local organic ash. Rheological studies were conducted in the lab to approximate the developed drilling mud systems' physical and rheological qualities. Laboratory analyses confirmed that organic ash improved the drilling mud's characteristics. It was determined how well organic ash performed in comparison to barite in a mud system. The results suggest that the mud systems with organic ash performed well in terms of l, gel strength, density, and pH, but poorly in terms of controlling filtering loss.

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118. Sharma, Praveen Kumar. “Some common fixed point theorems for sequence of self mappings in fuzzy metric space with property (CLRg).” J. Math. Comput. Sci., Vol.10, No.5 (2020): pp 1499-1509.
119. Sharma, Shivram, and Praveen Kumar Sharma. “On common α-fixed point theorems.” J. Math. Comput. Sci., Vol.11, No.1 (2020): pp 87-108.
120. Sharma, Praveen Kumar. “Common fixed point theorem in intuitionistic fuzzy metric space using the property (CLRg).” Bangmod Int. J. Math. & Comp. Sci., Vol. 1, No.1 (2015): pp 83-95.
121. Sharma, Praveen Kumar, S. Chaudhary, and Kamal Wadhwa. “Common Fixed Points For Weak Compatible Maps In Fuzzy Metric Spaces.” International Journal of Applied Mathematical Research, Vol.1, No. (2012): pp 159-177.
122. Sharma, Praveen Kumar, and Shivram Sharma. “Results on Complex-Valued Complete Fuzzy Metric Spaces.” Great Britain Journals Press, London Journal of Research in Science: Natural and Formal, Vol 23, Issue 2 (2023), Page No. 57-64.
123. D. K. Sharma, B. Singh, R. Regin, R. Steffi, and M. K. Chakravarthi, “Efficient Classification for Neural Machines Interpretations based on Mathematical models,” in 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), 2021.
124. F. Arslan, B. Singh, D. K. Sharma, R. Regin, R. Steffi, and S. Suman Rajest, “Optimization technique approach to resolve food sustainability problems,” in 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), 2021.
125. G. A. Ogunmola, B. Singh, D. K. Sharma, R. Regin, S. S. Rajest, and N. Singh, “Involvement of distance measure in assessing and resolving efficiency environmental obstacles,” in 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), 2021.
126. D. K. Sharma, B. Singh, M. Raja, R. Regin, and S. S. Rajest, “An Efficient Python Approach for Simulation of Poisson Distribution,” in 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), 2021.
127. K. Sharma, B. Singh, E. Herman, R. Regine, S. S. Rajest, and V. P. Mishra, “Maximum information measure policies in reinforcement learning with deep energy-based model,” in 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), 2021.
128. D. K. Sharma, N. A. Jalil, R. Regin, S. S. Rajest, R. K. Tummala, and Thangadurai, “Predicting network congestion with machine learning,” in 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC), 2021
129. Prince, Ananda Shankar Hati , Prasun Chakrabarti , Jemal Hussein , Ng Wee Keong , “Development of Energy Efficient Drive for Ventilation System using Recurrent Neural Network”, Neural Computing and Applications , 33 : 8659 , 2021.
130. Ashish Kumar Sinha, Ananda Shankar Hati , Mohamed Benbouzid , Prasun Chakrabarti , “ANN-based Pattern Recognition for Induction Motor Broken Rotor Bar Monitoring under Supply Frequency Regulation”, Machines , 9(5):87, 2021.
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132. Chakrabarti P. , Goswami P.S., “Approach towards realizing resource mining and secured information transfer”, International Journal of Computer Science and Network Security, 8(7), pp.345-350, 2008.
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134. Chakrabarti P., De S.K., Sikdar S.C., “Statistical Quantification of Gain Analysis in Strategic Management” , International Journal of Computer Science and Network Security,9(11), pp.315-318, 2009.
135. Chakrabarti P. , Basu J.K. , Kim T.H., “Business Planning in the light of Neuro-fuzzy and Predictive Forecasting”, Communications in Computer and Information Science , 123, pp.283-290, 2010.
136. Prasad A. , Chakrabarti P., “Extending Access Management to maintain audit logs in cloud computing”, International Journal of Advanced Computer Science and Applications ,5(3),pp.144-147, 2014.
137. Sharma A.K., Panwar A., Chakrabarti P. ,Viswakarma S., “Categorization of ICMR Using Feature Extraction Strategy and MIR with Ensemble Learning”, Procedia Computer Science, 57,pp.686-694,2015.
138. Patidar H. , Chakrabarti P., “A Novel Edge Cover based Graph Coloring Algorithm”, International Journal of Advanced Computer Science and Applications , 8(5),pp.279-286,2017.
139. Patidar H., Chakrabarti P., Ghosh A., “Parallel Computing Aspects in Improved Edge Cover based Graph Coloring Algorithm”, Indian Journal of Science and Technology ,10(25),pp.1-9,2017.
140. SS Priscila, M Hemalatha, “Improving the performance of entropy ensembles of neural networks (EENNS) on classification of heart disease prediction”, Int J Pure Appl Math 117 (7), 371-386, 2017.
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143. Sharma A.K., Panwar A., Chakrabarti P. and Viswakarma S., “Categorization of ICMR Using Feature Extraction Strategy and MIR with Ensemble Learning” In Proc. 3rd International Conference on Recent Trends in Computing (ICRTC) India. Mar 12-13(2015) p.41.
144. Garg A. , Ghosh A. and Chakrabarti P., “Gain and bandwidth modification of microstrip patch antenna using DGS,” In Proc. International Conference on Innovations in Control, Communication and Information Systems (ICICCI-2017), India. Aug 12-13,(2017)p.204.
145. P. Kumar, A. S. Hati, S. Padmanaban, Z. Leonowicz and P. Chakrabarti, “Amalgamation of Transfer Learning and Deep Convolutional Neural Network for Multiple Fault Detection in SCIM,” 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), Madrid, Spain, 2020, pp. 1-6.
146. Hung, B.T. and Chakrabarti, P. (2022) , “Parking Lot Occupancy Detection Using Hybrid Deep Learning CNN-LSTM Approach”, In Proc. 2nd International Conference on Artificial Intelligence: Advances and Applications. Algorithms for Intelligent Systems. Springer, Singapore.
147. S. Rajeyyagari, B. T. Hung and P. Chakrabarti, “Applications of Artificial Intelligence in Biomedical Image Processing,” 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), 2022, pp. 53-57.
148. P. Korsinwattana, “Efficiency enhancement of drilling mud by using fly ash as an additive,” Sut.ac.th:8080. [Online]. Available: http://sutir.sut.ac.th:8080/jspui/bitstream/123456789/5328/2/Fulltext.pdf. [Accessed: 18-Sep-2023].
Published
2023-10-26
How to Cite
Sneha H, Subha M, A. Nagarajan, & K. Bogeswaran. (2023). Effect of Organic Ash on Rheological Properties on Drilling Fluid. CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES, 4(10), 55-80. Retrieved from https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/532
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Articles