Performance Analysis of FastAPI Framework on Lost Circulation Handling Management Application in Oil Well Drilling
Abstract
Purpose: This study aims to conduct a load testing using JMeter and then analyze the performance of the FastAPI framework on the backend of the lost circulation handling management application in oil well drilling. The developed API receives input in the form of drilling parameter data (daily drilling report) from drilling engineers to be processed by a machine learning model (prediction and classification) through the FastAPI framework. The developed API returns processing data in JSON format.
Methodology: Performance measurement is done by conducting load testing simulations using the help of JMeter software. Load testing scenarios are created by varying the number of users and ramp-up time, as well as the method of loading the machine learning model used (normal or preemptive loading). The parameters measured in the test scenario are average execution time, maximum execution time, error percentage, and request throughput.
Findings: Load testing on a FastAPI-developed API demonstrated that for compute-heavy tasks like machine learning inference, increasing the number of processor cores and using preemptive model loading led to significantly better performance improvements than changes in processor clock speed or switching from HDD to SSD. Even when simulating a higher user load than initially expected (up to 250 users/threads), FastAPI maintained good response times and a low error rate, remaining below 20%.
Originality/value/state of the art: This study result is an information about the performance of the FastAPI framework in the application of lost circulation handling management in oil well drilling in the deployment phase, not only up to the model testing phase as in previous studies.
Keywords
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A. Lavrov, “Lost circulation: Mechanisms and solutions,” Lost Circ. Mech. Solut., pp. 1–252, Mar. 2016, doi: 10.1016/C2015-0-00926-1.
H. Elmousalami and I. Sakr, “Artificial intelligence for drilling lost circulation: A systematic literature review,” Geoenergy Sci. Eng., vol. 239, p. 212837, Aug. 2024, doi: 10.1016/J.GEOEN.2024.212837.
H. Pang, H. Meng, H. Wang, Y. Fan, Z. Nie, and Y. Jin, “Lost circulation prediction based on machine learning,” J. Pet. Sci. Eng., vol. 208, p. 109364, Jan. 2022, doi: 10.1016/J.PETROL.2021.109364.
A. T. Al-Hameedi et al., “Using Machine Learning to Predict Lost Circulation in the Rumaila Field, Iraq,” Oct. 2018, doi: 10.2118/191933-MS.
P. Pauhesti, A. Sembiring, M. Djumantara, L. Samura, C. Rosyidan, and K. Kunci, “EVALUASI PENANGGULANGAN LOST CIRCULATION LAPANGAN X,” PETRO J. Ilm. Tek. Perminyakan, vol. 12, no. 2, pp. 89–97, May 2023, doi: 10.25105/PETRO.V12I2.14383.
X. Hou et al., “Lost Circulation Prediction in South China Sea using Machine Learning and Big Data Technology,” Proc. Annu. Offshore Technol. Conf., vol. 2020-May, May 2020, doi: 10.4043/30653-MS.
A. Alsaihati, M. Abughaban, S. Elkatatny, and D. Al Shehri, “Application of Machine Learning Methods in Modeling the Loss of Circulation Rate while Drilling Operation,” ACS Omega, vol. 7, no. 24, pp. 20696–20709, Jun. 2022, doi: 10.1021/ACSOMEGA.2C00970/ASSET/IMAGES/LARGE/AO2C00970_0009.JPEG.
D. A. Wood, S. Mardanirad, and H. Zakeri, “Effective prediction of lost circulation from multiple drilling variables: a class imbalance problem for machine and deep learning algorithms,” J. Pet. Explor. Prod. Technol., vol. 12, no. 1, pp. 83–98, Jan. 2022, doi: 10.1007/s13202-021-01411-y.
V. Guillot, A. Ruzhnikov, M. Corona, and F. Karpfinger, “Machine Learning Prediction of the Lost Circulation Events at the Well Planning Stage,” Offshore Technol. Conf. Asia, OTCA 2024, Feb. 2024, doi: 10.4043/34764-MS.
C. V. Suresh babu, V. Surendar, E. Sriram, and S. Subhash, “Web-Based Deep Learning Model for Zero Day Vulnerability Detection using FastAPI,” 2024 Int. Conf. Adv. Data Eng. Intell. Comput. Syst., pp. 1–6, Apr. 2024, doi: 10.1109/ADICS58448.2024.10533540.
M. Raihan, S. Putra Pamungkas, M. Nurul Huda, D. A. Fauzan, A. Hilal Itsna, and M. Al-Hijri, “Sistem Klasifikasi Otomatis Dengan Konsep Machine Learning As A Service (MLaaS) Pada Kasus Pesan Berindikasi Cyberbullying,” Ilk. J. Comput. Sci. Appl. Informatics, vol. 4, no. 3, pp. 252–261, Dec. 2022, doi: 10.28926/ILKOMNIKA.V4I3.522.
P. Bansal and A. Ouda, “Study on Integration of FastAPI and Machine Learning for Continuous Authentication of Behavioral Biometrics,” 2022 Int. Symp. Networks, Comput. Commun. ISNCC 2022, 2022, doi: 10.1109/ISNCC55209.2022.9851790.
B. A. Baransel, A. Peker, H. O. Balkis, and I. Ari, “Towards Low Cost and Smart Load Testing as a Service Using Containers,” Commun. Comput. Inf. Sci., vol. 1382, pp. 292–302, 2021, doi: 10.1007/978-3-030-71711-7_24.
L. Cardoso Silva et al., “Benchmarking Machine Learning Solutions in Production,” Proc. - 19th IEEE Int. Conf. Mach. Learn. Appl. ICMLA 2020, pp. 626–633, Dec. 2020, doi: 10.1109/ICMLA51294.2020.00104.
N. Husufa and I. Prihandi, “Optimizing JMeter on Performance Testing Using the Bulk Data Method,” J. Inf. Syst. Informatics, vol. 4, no. 2, pp. 205–215, Jun. 2022, doi: 10.51519/JOURNALISI.V4I2.244.
E. N. Alam and F. Dewi, “PERFORMANCE TESTING ANALYSIS OF BANDUNGTANGINAS APPLICATION WITH JMETER,” Int. J. Innov. Enterp. Syst., vol. 6, no. 02, pp. 157–166, Jul. 2022, doi: 10.25124/IJIES.V6I02.172.
R. Dhuny, A. A. I. Peer, N. A. Mohamudally, and N. Nissanke, “Performance evaluation of a portable single-board computer as a 3-tiered LAMP stack under 32-bit and 64-bit Operating Systems,” Array, vol. 15, p. 100196, Sep. 2022, doi: 10.1016/J.ARRAY.2022.100196.
DOI: https://doi.org/10.31315/telematika.v21i1.13259
DOI (PDF): https://doi.org/10.31315/telematika.v21i1.13259.g6611
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