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All Journal POSITRON Aceh International Journal of Science and Technology Jurnal Geosaintek
Saroji, Sudarmaji
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Energy Physics

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Comparison of Hydrocarbon Volumetric Calculation between Cell-Based Model and Numerical Integration Oktaviani, Vania; Saroji, Sudarmaji; Trisna, Muhammad Destrayuda
POSITRON Vol 14, No 1 (2024): Vol. 14 No. 1 Edition
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam, Univetsitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/positron.v14i1.61374

Abstract

Volume estimation for hydrocarbon reserves is a challenging yet pivotal task in engineering for exploration and production. Advances in technology now enable us to compute volume integration using programming computation. Various approaches using numerical integration, including the trapezoidal, pyramidal, and Simpson's rule, along with cell-based models as comparative methods can be used for the calculation of hydrocarbon volume. In this study, original oil in place (OOIP) is employed to determine reserve oil volume.   The OOIP values obtained are 8.55 million m3 through cell-based calculations, 8.63 million m3 via the trapezoidal approach, 8.58 million m3 using the pyramidal method, and 8.57 million m3 with Simpson's 3/8 rule. The relative error ratio percentages between the cell-based model as the reference value and the numerical integration calculations as the measured values are 0.93% for the trapezoidal method, 0.35% for the pyramidal method, and 0.23% for Simpson's 3/8 rule. Simpson's 3/8 rule demonstrates the closest mathematical result to the cell-based model. Within this margin of error, the methodologies have been demonstrated to proficiently compute hydrocarbon reserves from real data through simplified and abbreviated processes.
Implementation of Deterministic and Multimineral Method in Petrophysical Analysis for Identifying Low Resistivity Reservoir in Tesla Field, Air Benakat Formation, South Sumatera Basin Saroji, Sudarmaji; Trihapsari, Wandia Mellani; Trisna, Muhammad Destrayuda
Aceh International Journal of Science and Technology Vol 13, No 3 (2024): December 2024
Publisher : Graduate School of Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/aijst.13.3.36698

Abstract

The Tesla field is located in the South Sumatra Basin, where there is the Air Benakat Formation, with the constituent rocks being dominated by alternating sandstone and claystone so that it is a shaly sand environment with the potential to become a low-resistivity hydrocarbon reservoir. Hydrocarbon reservoirs generally have a resistivity log value of more than 10 m; when a hydrocarbon reservoir has a low-resistivity value between 0.5 - 5 m, it is referred to as a low-resistivity hydrocarbon reservoir. Initially, deterministic analysis was carried out to calculate the petrophysical parameters of the potentially low-resistivity reservoirs. However, the results show a low validation value of petrophysics parameters, such as effective porosity and water saturation, when compared to the DST data, so a multimineral analysis is carried out to increase the validation value of the petrophysical parameters. The use of the multimineral method has produced the petrophysics parameter closer to DST Data when compared to the petrophysics parameter produced by the deterministic method in Tesla Field. The formation analysis shows that the low resistivity reservoir in the Tesla Field is caused by the grain size of the sandstone, which is very fine so that it can bind water significantly (irreducible water), abundant shale content, and distributed by lamination of shale, dispersed shale, and structural shale as well as the presence of conductive glauconite minerals.
EFFECTIVE POROSITY PREDICTION FROM WELL LOG DATA USING SUPPORT VECTOR MACHINE (SVM) Saroji, Sudarmaji; Haqqi, Muhammad Fajrul; Prakoso, Suryo
Jurnal Geosaintek Vol. 11 No. 1 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25023659.v11i1.1936

Abstract

Support Vector Machine (SVM) algorithm is a machine learning method renowned for its high accuracy and computational efficiency in prediction and classification tasks. In this study, SVM was applied to predict effective porosity from well log data. The prediction model was optimized using GridsearhCV (GS CV) module and tested on seven wells from the 'Mentari' field, Indonesia. Six variations of training-testing configuration were evaluated to assess the prediction performance. The best ere acjieved using four training wells and three testing wells, yielding  an accuracy of 71% with training time of 1.98 seconds. The analysis revealed that increasing the volume of training data improves accuracy, albeit with longer computational time. This study confirms that SVM demonstrates strong predictive capability for effective porosity and has the potential to serve as a supporting tool in simplifying geological interpretation, particularly during the initial analysis stage when sufficient training data is available.
Co-Authors Haqqi, Muhammad Fajrul Oktaviani, Vania Suryo Prakoso, Suryo Trihapsari, Wandia Mellani Trisna, Muhammad Destrayuda