PETROPHYSICAL ANALYSIS AND SEISMIC STRATIGRAPHY INTERPRETATION TO DETERMINE HYDROCARBON RESERVOIR IN TARAKAN BASIN, BUNYU ISLAND WATERS

Tarakan Basin area of Bunyu Island Waters is known to have hydrocarbon potential with complex geological structures. This study aims to determine reservoir characterization and to obtain prospect of hydrocarbon reservoir zones based on petrophysical and seismic stratigraphy analysis with reference to Well DDS-1 and 2D seismic Line S88. Petrophysical analysis results 3 zones that have potential as hydrocarbon reservoirs. Based on petrophysical quantitative analysis, Zone 1 has values of 52.25% for shale volume, 18.48% for effective porosity, 39.84% for water saturation and 13.03 mD for permeability. Zone 2 has values of 54.66% for shale volume, 10.27% for effective porosity, 40.9% for water saturation and 1.14 mD for permeability. Zone 3 has values of 49.22% for shale volume, 9.33% for effective porosity, 56.33% for water saturation and 0.22 mD for permeability. Out of these three reservoir zones in Well DDS- 1, Zone 1 has the prospect of hydrocarbons which is supported by the net pay value. Based on seismic stratigraphy interpretation, the reservoir zone is correlated to the Tabul Formation, which comprises calcareous clay and limestone.

Petrophysical Analysis of ‘Bright’ Field, Onshore Niger Delta

Petrophysical analysis is key to the success of any oil exploration and exploitation work and this task requires evaluation of the reservoir parameters in order to enhance accurate estimation of the volume of oil in place. This research work involves the use of suite of well logs from 4-wells to carry out the petrophysical analysis of ‘Bright’ Field Niger Delta. The approach used includes lithology identification, reservoir delineation and estimation of reservoir parameters. Two sand bodies were mapped across the entire field showing their geometry and lateral continuity, gamma ray and resistivity logs were used to delineate the reservoirs prior to correlation and relevant equations were used to estimate the reservoir parameters. The result of the petrophysical analysis showed variations in the reservoir parameters within the two correlated sand bodies with high hydrocabon saturation in sand 1 well 1 while the remaining wells within the correlated wells are water bearing. The porosity values range from 0.19 to 0.32, volume of shale from 0.15 to 0.40, water saturation from 0.20 to 0.92 for the sand bodies.

The Application of Maine Petrophysical Method; Adding Resources by Explore the Opportunity in Low Resistivity Pay Reservoir

Manifestation of Low Resistivity Pay (LRP) Existences in ONWJ Area because of Fine Grained, Superficial Microporosity, Laminated Shaly Sand and Electronic Conduction. Water saturation petrophysical analysis for LRP Case due to those reason above can be solved by electrical parameter determination with Type Curve. But to overcome the LRP caused by Laminated Shaly Sand, the use of high resolution resistivity logs that are close to the resolution of thin bed reservoir is a must. Alternative solutions, conventional high resolution resistivity logs, namely Micro Spherical Focused Log (MSFL) are used to interpret thin bed reservoirs that have the hydrocarbon potential. This intergrated petrophysical analysis is called MAINE Petrophysical Method The Petrophysical MAINE method is the development of the TECWAL (Type Curve, Core and Water Analysis) method which leaves question marks on Laminated Shaly Sand Reservoir and the possibility of variations in the Electrical Parameter and Water Saturation Irreducible (SWIRR) dependent on Rocktype. The Basis of the MAINE Method is the Worthington Type Curve with some assumptions such as Each rocktype has a different value of Bulk Volume of Water (BVW) and BVW can be used to determine the SWIRR value of each rocktype and Each rocktype has different electrical parameter m and n. In the process, the use of J-Function and Buckles Plot is applied to help determinet Rocktype and BVW values. The rocktype will be the media in distributing the value of Electrical Parameter generated by the Type Curve and the value will be used in water saturation calculation. In Laminated Shaly Sand Reservoir, Rocktyping will be analyzed more detail using the High Resolution Conventional Log, Micro Spherical Focused Log (MSFL). The expected final result of this analysis is the more reliable Water Saturation (SW) and the integration of water saturation values in the Buckles Plot which can help in determining the transition zone in order to avoid mistakes in determining the perforation zone. Through the MAINE Petrophysical Method, there is a decrease in water saturation from an average value 86% to 66% or a decrease 23%. This result is quite significant for the calculation of reserves in the LRP zone. By integrating this method with the Buckles Plot, it can help the interpreter to determine the perforation interval in order to avoid water contact or the transition zone

ntegration of Petrophysical Analysis and Elastic Log Properties as an Input to Optimize the Development Wells Target in Unique Globigerina Limestone Gas Reservoir in Madura Strait

The Madura Strait PSC is located in the southern part of North East Java Basin with biogenic gas from Globigerina limestone Pliocene Mundu and Selorejo sequence as main target. At early stage of field development,understanding and knowledge about petrophysical and elastic properties of reservoir rock quality is required and very important. The petrophysical analysis provide properties such as clay volume, porosity, permeability, water saturation and mineral volume to separate reservoir and non-reservoir zone. The elastic rock properties such as acoustic impedance (AI), shear impedance (SI), velocity ratio (Vp/Vs), and Poisson’s ratio (σ) were generated to identify clay zone, gas and non-gas also focused to distinguish reservoir rock quality inside gas zone as an effective reservoir characterization. This research is done by utilize core data, quad combo logs from eleven wells and shear velocity from eight wells. The purpose of this research is to optimize development well target in Globigerina limestone gas reservoir, which have good to best reservoir rock quality shown with high porosity and permeability,low clay volume and water saturation. Results from this research indicate that lime mud matrix have significant impact in the reservoir rock quality. Meanwhile, gas saturation can affect the elastic properties due to this high gas saturation can decrease compressional velocity (Vp) value. Finally, the integration of petrophysical result and combination of elastic properties implementation can help in distinguishing the best reservoir rock quality, which contains gas that should be penetrated by the development wells

NOVEL METHODOLOGY FOR AUTOMATION OF BAD WELL LOG DATA IDENTIFICATION AND REPAIR

Subsurface analysis-driven field development requires quality data as input into analysis, modelling, and planning. In the case of many conventional reservoirs, pay intervals are often well consolidated and maintain integrity under drilling and geological stresses providing an ideal logging environment. Consequently, editing well logs is often overlooked or dismissed entirely. Petrophysical analysis however is not always constrained to conventional pay intervals. When developing an unconventional reservoir, pay sections may be comprised of shales. The requirement for edited and quality checked logs becomes crucial to accurately assess storage volumes in place. Edited curves can also serve as inputs to engineering studies, geological and geophysical models, reservoir evaluation, and many machine learning models employed today. As an example, hydraulic fracturing model inputs may span over adjacent shale beds around a target reservoir, which are frequently washed out. These washed out sections may seriously impact logging measurements of interest, such as bulk density and acoustic compressional slowness, which are used to generate elastic properties and compute geomechanical curves. Two classifications of machine learning algorithms for identifying outliers and poor-quality data due to bad hole conditions are discussed: supervised and unsupervised learning. The first allows the expert to train a model from existing and categorized data, whereas unsupervised learning algorithms learn from a collection of unlabeled data. Each classification type has distinct advantages and disadvantages. Identifying outliers and conditioning well logs prior to a petrophysical analysis or machine learning model can be a time-consuming and laborious process, especially when large multi-well datasets are considered. In this study, a new supervised learning algorithm is presented that utilizes multiple-linear regression analysis to repair well log data in an iterative and automated routine. This technique allows outliers to be identified and repaired whilst improving the efficiency of the log data editing process without compromising accuracy. The algorithm uses sophisticated logic and curve predictions derived via multiple linear regression in order to systematically repair various well logs. A clear improvement in efficiency is observed when the algorithm is compared to other currently used methods. These include manual processing by a petrophysicist and unsupervised outlier detection methods. The algorithm can also be leveraged over multiple wells to produce more generalized predictions. Through a platform created to quickly identify and repair invalid log data, the results are controlled through input and supervision by the user. This methodology is not a direct replacement of an expert interpreter, but complementary by allowing the petrophysicist to leverage computing power, improve consistency, reduce error and improve turnaround time.