Mohammad Barezzi Naufal Taftazzanni

LOG DATA ANALYSIS ON LOW RESISTIVITY RESERVOIR CASE IN Z FIELD FOR DETERMINATION OF PRODUCTIVE ZONE

Introduction

Log data analysis on low resistivity reservoir case in z field for determination of productive zone. Analyze log data in Z Field's low resistivity reservoirs to identify productive zones. Discover clay minerals and laminated shale cause low resistivity. Petrophysical calculations use Indonesian, Simandoux, and Archie methods for water saturation.

Abstract

AbstractWell Logging is a job to get subsurface data. The anomaly on the results of reading log data is one of the cases that often occurs. Low resistivity reservoir is an example. In Field Z there were 6 wells studied. In these wells in the Telisa formation it is indicated that the reservoir zone has a low resistivity (1-7 Ωm). This is indicated by the existence of a good oil show, namely the presence of fluorescence, stain and cut for the six wells based on mud log data. However, based on log data, this zone has a low resistivity. This anomaly has caused this reservoir zone to be overlooked during the initial exploration period. Therefore, it is necessary to analyze the core data in the area indicated by low resistivity in order to find out the cause of the low resistivity value. The presence of clay minerals is the main factor causing low resistivity values. Based on XRD data, the composition of clay minerals in the research wells ranged from 11-47% including Smectite minerals, Illite minerals, kaolinite minerals and chlorite minerals. The conductive properties of clay minerals are the cause of the low resistivity value. In addition, based on the analysis of the shale distribution in the Telisa formation is a type of laminated shale. The presence of shale content in sandstone can bind water significantly (clay bound water) thus reducing the reading of the resistivity value. In addition to determining and analyzing the low resistivity reservoir zone, petrophysical calculations are also carried out. For the calculation of water saturation in this study carried out using three methods, namely the Indonesian method, Simandoux and Archie. Based on the results of the validation of water saturation calculations on core data, it was found that the Indonesian method is the best method that matches the value of water saturation, which are 59%, 56.1%, 60.2%, 59.4%, 61.1% and 62.3%.Keywords: Well logging, low resistivity, XRD, oil show, clay mineral

Review

This paper tackles a crucial and frequently encountered challenge in subsurface exploration: the accurate identification of productive zones in low resistivity reservoirs. The authors present a compelling case study from Field Z, where six wells within the Telisa formation exhibit reservoir zones with unusually low resistivity values (1-7 Ωm), despite clear indications of hydrocarbons through mud log data (oil shows, fluorescence, stain, cut). This anomaly, which historically led to these zones being overlooked, underscores the practical significance of the study. The research effectively highlights the necessity of integrated data analysis, moving beyond conventional log interpretations to prevent the bypass of potentially significant reserves. The methodology employed in the study is robust, combining mud log observations, core data analysis, and advanced petrophysical calculations. A key strength lies in the use of X-ray Diffraction (XRD) data, which conclusively identifies the presence and composition of clay minerals (ranging from 11-47%, including Smectite, Illite, Kaolinite, and Chlorite) as the primary cause of the low resistivity. Furthermore, the analysis differentiates between clay types, noting their conductive properties and the impact of laminated shale distribution and clay-bound water on resistivity readings. The study meticulously evaluates three common water saturation methods – Indonesian, Simandoux, and Archie – validating their accuracy against core data. The finding that the Indonesian method provides the best match for water saturation values (59-62.3%) is a significant practical contribution for future reservoir characterization in similar geological settings. Overall, this paper provides a valuable contribution to the field of formation evaluation, particularly concerning low resistivity pay zones in clastic reservoirs. The integrated approach, combining mud logging, core analysis, mineralogical studies (XRD), and petrophysical modeling, offers a comprehensive framework for re-evaluating overlooked hydrocarbon bearing zones. The clear identification of clay minerals and laminated shale as the root causes, along with the validation of a suitable water saturation model, makes this study highly applicable. This research successfully demonstrates that proper re-evaluation can unlock significant reserves, providing a strong argument for similar integrated studies in other complex reservoir environments.

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