Low Resistivity Reservoirs Research Articles

Identification of the low resistivity-low contrast (LRLC) gas-bearing pay zones in Shaly sand reservoirs using acoustic data: a case study from the Messinian Abu Madi formation, onshore Nile Delta, Egypt

AbstractDetection of the low resistivity-low contrast (LRLC) reservoirs is among the main challenges in the oil industry. In this concern, the LRLC pay zones of the Upper Messinian Abu Madi clastic reservoirs in the onshore Nile Delta Gas fields became a main challenge for significant exploration. This type of reservoirs, including low resistivity-low contrast zones and thin-bedded intervals, are often overlooked using the conventional petrophysical evaluation techniques, especially in the wildcat exploratory wells or highly agitated shoreline depositional environments like the Nile Delta of Egypt. These hidden low contrast reservoirs are generally challenging due to the presence of many shale intercalations/laminations and/or due to increasing the shale volume represented in the form of dispersed distribution, and the dominance of conductive clay minerals. Therefore, in this study, the expected high resistivity values of the gas-bearing reservoir intervals of the Abu Madi Formation in the onshore Begonia gas Field, as a typical case study of the LRLC reservoirs, are masked due to the relatively high shale conductivity, particularly when the thickness of these intervals is less than the vertical resolution of the utilized conventional resistivity log. To verify the LRLC phenomena of the Begonia gas Field, the obtained data was compared to the South Abu El Naga gas Field as a normal case study with a relatively high resistivity gas-bearing pay zone. To overcome the impact of the conductive clay mineral content and identify these hidden low resistivity reservoir intervals, it is necessary to integrate the conventional logging data (gamma-ray, shallow and deep resistivity, density, and neutron) with the acoustic log data including shear and compressional sonic data. In this way, a useful relationship can be established enabling the detection of these hidden LRLC reservoir intervals. This integration is based on the principle that shear waves are not influenced by the fluids types, whereas the compressional sonic waves are influenced by the reservoir fluids. However, to effectively investigate these concealed LRLC reservoir intervals, which can boost production and increase the potential reserves, it is essential to have a low water cut value. The present study represents introduces an efficient workflow, which can be extended to other similar LRLC pay zones in the Nile Delta and northeast Africa. It is also extendible to the LRLC reservoirs in similar deltaic systems having conductive minerals-bearing reservoirs or thin beds.

Genetic Analysis of Chang 2 Low Resistivity Reservoir in Jiyuan Area of Ordos Basin

Genetic Analysis of Chang 2 Low Resistivity Reservoir in Jiyuan Area of Ordos Basin

Geothermal resources and deep tectonic in Leh Ladakh (NW Himalaya), India: Inference from magnetotelluric studies

The geothermal resources and their connectivity with the deep tectonic are meaningful to understand through their structural geometry in the NW Himalayas. Therefore, over the NW-SE profiles, 23 audio Magnetotelluric (AMT) sites and over the SW-NE profile, 15 broadband Magnetotelluric (BBMT) sites were carefully chosen to provide the detailed structure. The electrical models were produced from a 2-D inversion algorithm based on the non-linear conjugate gradient method. The Chumathang-Mahe and Puga-Sumdo regions offer excellent geothermal potential as heat/fluid passes through the Mahe Fault (MF), Zildat Fault (ZF) and Kiagor Tso Fault (KTF). These faults are well revealed in the electrical models beneath the subsurface. The Puga Valley has an excellent geothermal resource at shallow depth. A ∼1000 m thick sulfide mineral body is defined with high conductivity (∼1 Ω-m). The crustal structure shows a highly conductive mid-crust beneath the Ladakh batholiths with no manifestation roots. The signs of the Indus-Tsangpo Suture Zone (ITSZ) and Shyok Suture Zone (SSZ) are well represented in the crustal model and are associated with the steep-dipping faults. The Chumathang-Mahe and Puga-Sumdo geothermal regions are connected with low resistivity body (C1) at shallow depths in the crustal model along the SW-NE profile and offer a path for deep fluid flow. The frictional heat generated in the process of the Indian plate subducting beneath the Tibetan plateau and the collision zone between India and Asia carried to melt. The high heat surface flow indicates the thermal origin of low resistivity. Thus, a ∼10 sq. km low resistivity reservoir is identified beneath the Tso Morari dome in the crustal model that overlies a resistivity feature. The high conductivity at mid-crust determined at active tectonic fabric may be potential geothermal resources yielding high well-productivity.

Study on Main Controlling Factors and Fluid Identification Methods of Low-resistivity Reservoirs based on BP Nerve Network Verification in X Area of Ordos Basin

The low-resistivity oil layers refer to a formation whose resistivity response is opposite to the typical oil layers and similar to water layer. Distinguishing it from water layer in logging interpretation is very difficult, which brings great difficulties to oil and gas exploration and development. The exploration and development research shows that there is obvious resistivity anomaly in X area of Ordos Basin, and typical low-resistivity oil layers are developed in formation A in this area, so it is difficult to accurately divide oil layers and water layers only from logging response. In order to identify the fluid properties in low-resistivity oil layer areas, based on conventional logging data of formation A in X area, through petrophysical experimental analysis, combined with formation water analysis data, logging, oil test and other data, the main control factors of low-resistivity oil layers in X area are deeply studied in many aspects, and a more effective fluid identification method is put forward by combining the secondary evaluation of porosity and permeability and the flow unit method. The BP nerve network simulation cross plot is used to verify the identification results. The research shows that the main control factors of low resistivity oil layers in the study area are related to the salinity of water and pore connectivity, and also to the lithologic properties of different formations. Based on the study of the main control factors of low-resistivity oil layers, a multi-factor fluid identification method is proposed. The coincidence rate between the evaluation results of this identification method and oil test is 85.7%, which can identify the oil-water reservoirs in X area more effectively and accurately, finally identify the location of low-resistivity oil layers and boost the growth rate of oil and gas production.

Petrophysics of Low Resistivity Reservoir

Low resistivity reservoirs have long been known in the oil and gas industry as reservoirs that have significant potential and contribution to oil and gas production. This reservoir is very unique because of its relatively low resistivity readings (< 5 Ohm) and often other log responses do not show the way carbonate-containing reservoirs do, so they are often overlooked and not considered as potential carbon reservoirs. The low resistivity reading also causes the Water Saturation (Sw) value to be too high, which contributes to why the reservoir is overlooked. Theoretically, the factors that cause low resistivity readings are high clay/shale content, fine grain size, high salinity air formation, presence of microporosity and presence of conductive minerals which affect resistivity log readings in addition to other things such as low structured reservoirs. and there is a significant slope of the layers. Based on statistic reports, generally in low resistivity reservoirs there is a combination of two or more of the causal factors mentioned. This study aims to identify the characteristics of low resistivity reservoirs in the Upper Cibulakan Formation, West Java Basin, especially from log readings and recognition of what factors support reservoir formation both from the results of core and log analysis as well as selecting combinations for calculating water saturation. From the low resistivity reservoir characteristics, it is hoped that it can be used to find and identify the distribution of similar reservoirs so that potential reservoirs that have been overlooked so far can be identified, discovered and developed which in the end are expected to provide additional contributions both in terms of education and for the oil and gas industry in finding new reserves for oil and gas production in Indonesia.

The Comparation of Water Saturation Approaches to Reveal a Low Resistivity Reservoir Potential Case in Gumai Formation, South Sumatra Basin

The LRLC reservoir zone has been identified in SN-3 well, at the DAP-1 interval on Gumai Formation. This interval has a low resistivity value from 3-5 ohm.m and the drill stem test (DST) results show oil with gas without water. This study is objected to identify the causes of LRLC reservoir in gumai formation and finding a suitable sw calculation method. Some data such as well logs, reports, cores, and XRD are used to calculate petrophysical parameters such as Vsh, Phie, and Sw, and would be validated by DST data. Water saturation (Sw) calculations from Archie and the CEC method (Waxman Smits, Dual Water, Juhasz) were performed and the results were compared. The results showed that the main cause of the DAP-1 interval LRLC zone was the presence of clay minerals consisting of mixed layers (Illite/smectite). These clay minerals will be associated with high cation exchange capacity (CEC) values, with the value 70 (meq/100g), which can increase conductivity and reduce resistivity values. Based on lumping the more optimistic results of sw calculation from Waxman Smits Sw method (Sw based on CEC method). The DST data on the SN-3 well does not have water test data, so the calculation of the Sw value that is close to the Swirr value is considered the most suitable Sw for the low resistivity reservoir conditions of the Gumai Formation in the study area. The best practice for low resistivity reservoir for suitable petrophysical calculation is necessary to pay attention to the rock lithology conditions, the presence of mineral clay, and determining suitable Sw appropriate to the reservoir conditions

Water Saturation Prediction in the Middle Bakken Formation Using Machine Learning

Tight reservoirs around the world contain a significant volume of hydrocarbons; however, the heterogeneity of these reservoirs limits the recovery of the original oil in place to less than 20%. Accurate characterization is therefore needed to understand variations in reservoir properties and their effects on production. Water saturation (Sw) has always been challenging to estimate in ultra-tight reservoirs such as the Bakken Formation due to the inaccuracy of resistivity-based methods. While machine learning (ML) has proven to be a powerful tool for predicting rock properties in many tight formations, few studies have been conducted in reservoirs of similar complexity to the Bakken Formation, which is an ultra-tight, multimineral, low-resistivity reservoir. This study presents a workflow for Sw prediction using well logs, core data, and ML algorithms. Logs and core data were gathered from 29 wells drilled in the Bakken Formation. Due to the inaccuracy and lack of robustness of the tried and tested regression models (e.g., linear regression, random forest regression) in predicting Sw as a continuous variable, the problem was reformulated as a classification task. Instead of exact values, the Sw predictions were made in intervals of 10% increments representing 10 classes from 0% to 100%. Gradient boosting and random forest classifiers scored the best classification accuracy, and these two models were used to construct a voting classifier that achieved the best accuracy of 85.53%. The ML model achieved much better accuracy than conventional resistivity-based methods. By conducting this study, we aim to develop a new workflow to improve the prediction of Sw in reservoirs where conventional methods have poor performance.

LOW RESISTIVITY PAY DEVELOPMENT: CASE STUDY OF TALANGAKAR FORMATION ASRI BASIN, OFFSHORE SOUTHEAST SUMATRA, INDONESIA

Southeast Sumatra is a prolific oil and gas block located offshore in the Java Sea, 90 km north of Jakarta Bay. This area covers two major basins, namely Sunda Basin and Asri Basin. The initial development of the area focused on faulted and high closures and high-resistivity reservoirs. Further analysis shows that there are special low-resistivity reservoirs in Widuri Area, especially in the Aryani field. This paper will discuss the low resistivity pay zone reservoirs and fluid containment of the intervals. The paper will also include further assesment this undeveloped interval to increase oil production, considering the upside potential of the reservoirs using current geological, geophysical, and reservoir engineering approaches. Additionally, it will describe the operational challenges faced during the production period. The low-resistivity pay zone, a hydrocarbon-bearing reservoir in the Aryani field of the Widuri area, was identified using gas readings in the daily drilling reports, complemented with mud logging data. The Basal Sand interval, which drapes above the basement, was the site of the first producing well of Basal Sand, Aryani AC-X, preceded by a hydraulic fracturing job. Lambda-mu-rho inversion was implemented to delineate this reservoir. To recognize the potency in those wells, data reconfirmation was conducted between the elan summary, sidewall core, and cutting data. Aryani AC-X has successfully drained oil using a submersible pump, with an initial production of 408 bopd and cumulative production of 240 MBO.

Genetic Mechanisms and Multiparameter Logging Identification of Low-Resistivity Oil Pay: A Case Study of the Triassic Chang 6 Member, Zhidan Area, Ordos Basin, China.

Low-resistivity pay has been found throughout the world. The causes and logging responses of low-resistivity reservoirs are complex and variable. The weak variations in resistivity between the oil pay and the adjacent water pay makes it difficult to identify fluids by resistivity log analysis, which reduces the overall exploration benefit of the oil field. Therefore, it is very important to study the genesis and logging identification technology of the low-resistivity oil pay. In this paper, we first analyzed the core results such as X-ray diffraction, scanning electron microscopy, mercury intrusion, phase permeability, nuclear magnetic resonance, physical properties, electric petrophysical experiment, micro-CT, rock wettability, etc. The results show that: ① the development of low-resistivity oil pays in the studied area is mainly controlled by irreducible water saturation. The complicated pore structure, high gamma ray sandstone, and rock hydrophilicity are the factors that lead to the increase of irreducible water saturation. ② The salinity of formation water and the invasion of drilling fluid also have a certain influence on the variation of reservoir resistivity. ③ In order to magnify the difference between oil and water, sensitive parameters of logging response are extracted according to the controlling factors of low- resistivity reservoir. Then, AC-RILD, SP-PSP, GR*GR*ΔSP-RILD, and (RILM-RILD)/RILD-RILD cross-plots, some overlap method, and movable water analysis are used to identify low-resistivity oil pays synthetically. In the case study, the comprehensive application of the above identification method can effectively improve the accuracy of fluid recognition step by step. It provides reference for identifying more low-resistivity reservoirs with similar geological conditions.

Finite element method-based resistivity simulation and water saturation calculation of irregular laminated shaly sandstone

Shaly sandstone reservoir is one of the most significant targets in petroleum and gas exploration. However, the influences of various factors on the resistivity of irregular laminated shaly sandstone are yet to be determined, and it is extremely challenging to accurately calculate the water saturation. By considering shaly sandstone in Zhujiang Formation of Neogene in Pearl River Mouth Basin as an example, this research extracts the shale distribution form and the pore structure by image processing, simulates the resistivity of rock by finite element method, analyzes the influence of shale parameters on resistivity, and deduces the water saturation equation of shaly sandstone. Results show that, in shaly sandstone, shale distributes in irregular laminated patterns on a millimeter scale. The other clean sandstone areas have high porosity and the capacity to reserve oil and gas. At high water saturation states, the shaly sandstone mainly conducts electricity in the clean sandstone area and various shale parameters have minor influences on the resistivity of shaly sandstone. At low water saturation states, the shaly sandstone mainly conducts electricity in the shale area, the resistivity of shaly sandstone is very close to the resistivity of the water layer, and the reservoir is the so-called low resistivity reservoir. The conductive form of clean sandstone area and shale laminae tends to parallel but remains a noticeable difference from total parallel. The simulation results deduced that the water saturation equation of shaly sandstone is more accurate than other equations, which provides an innovative mindset to calculate the water saturation of shaly sandstone.

Study on Geological Genesis and Sedimentary Model of Complex Low Resistivity Reservoir in Offshore Oilfield — A Case of NgIII Formation of X Oilfield in Bohai Sea

Study on Geological Genesis and Sedimentary Model of Complex Low Resistivity Reservoir in Offshore Oilfield — A Case of NgIII Formation of X Oilfield in Bohai Sea

Reconstruction of LWD-NMR T2 water spectrum and fluid recognition based on microscopic pore structure constraints

The existing fluid property identification methods are not applicable to low resistivity reservoirs. In order to accurately identify low-resistivity oil reservoirs, the effects of fluid properties on the NMR T2 spectrum under different pore structures were analyzed. The results show that the T2 spectrum of the low-resistivity oil section shows a broad and gentle hilly shape, and the T2 spectrum of the water section shows a steep and mountainous shape. Based on the difference characteristics of T2 spectrum of reservoirs with different fluid properties, a new NMR water spectrum reconstruction and fluid property identification method based on microscopic pore structure constraint is innovatively proposed. Based on the drilling NMR logging data, three parameters characterizing the microscopic pore structure are firstly extracted from the NMR T2 spectrum of standard water reservoirs, which reflect the overall shape of the T2 spectrum in terms of transverse relaxation time, longitudinal pore fraction and These three parameters reflect the pore structure, water and oil and gas information of the reservoir in terms of the transverse relaxation time, the longitudinal pore fraction and the overall morphology of the T2 spectrum. Secondly, the microscopic pore structure parameters are used as the coordinate axes to construct a 3D water spectrum sample library, and the standard water formation T2 spectrum samples of the target block are stored in the 3D water spectrum library through the constraints of the three parameters. Finally, the NMR T2 spectrum parameters are extracted from the whole well section and the samples in the water spectrum library are called to realize the water spectrum reconstruction. The parameters that can distinguish oil and water formations are extracted from the measured T2 spectrum and the reconstructed water spectrum and fluid identification is performed. The results show that the reconstructed water spectrum is highly similar to the actual measured T2 spectrum in the production-proven standard water layer, which verifies the accuracy of the water spectrum reconstruction technique; in Caofeidian A oilfield, the fluid identification was performed on 20 production-proven test sections, of which 18 reservoirs were correctly identified, and the identification compliance rate reached 90%. The method circumvents the influence of pore structure in NMR T2 spectrum and highlights the information of its fluid properties, which improves the fluid property identification capability of logging with drilling (LWD) NMR logging.

Uncovering The Potential of Low Resistivity Reservoirs Through Integrated Analysis: A Case Study from The Talang Akar Formation in The South Sumatra Basin

The study area is an oil and gas field that has a low resistivity reservoir zone in the Talang AkarFormation, South Sumatera Basin. The reservoir zone is composed of siltstone and sandstone that was depositedin a shallow to transitional marine environment. The aim of this study is to identify and determine the potential oflow resistivity reservoirs in the study formation. The data used in the study include well log, mud log, core data(porosity, permeability, petrography), formation water analysis, and biostratigraphy. The methodology includesqualitative analysis to determine reservoir potential zones (quick look interpretation) and the reason for low resistivityreservoirs, as well as quantitative analysis to calculate petrophysical parameters. The results of the quicklook analysis showed 77 low resistivity reservoir zones, but the petrophysical analysis identified 25 low resistivityreservoir zones as potential candidates for drill steam test (DST). Factors causing low resistivity reservoirs inthe study area include clay mineral content (kaolinite and illite), conductive minerals (pyrite and siderite), highsalinity formation water (10,000 ppm), very fine to medium sand grain size (0.063 - 0.5 mm) distribution ofclay minerals (laminated and dispersed clay), and thin laminates. The results of the petrophysical analysis showthat shale volume is less than 30%, porosity is greater than 14%, permeability is greater than 10 mD, and watersaturation is less than 70%.

Petrofacies-Dependent Cementation Factor Relationship for Low-Resistivity Miocene Carbonates: Mumbai Offshore Basin

Summary In Mumbai offshore, Miocene carbonates are deposited with intermediate clastic inputs under cyclic sea level changes and have undergone diagenesis from time to time. Miocene carbonate layers deposited southwest of Mumbai High are producing a good amount of hydrocarbon from 1 to 2 Ω·m resistivity pays. A total of 58 representative core plugs from four different wells were studied to identify the reason for low resistivity and to classify rock facies types and porosity systems using scanning electron microscopy (SEM), thin-section nuclear magnetic resonance (NMR), and petrophysical core data. It was observed from the core study that Miocene carbonates have complex porosity systems and mud-supported to grain-supported reservoir facies. Dominance of mud-supported matrix is the main reason for low resistivity in Miocene carbonate layers as observed from integrated advanced log and core studies. Conventional petrophysical evaluation using constant petrophysical parameters (a, m, n) or linear correlation of cementation factor with porosity can lead to erroneous results in this scenario. A petrofacies-dependent correlation among cementation factor and porosity is attempted in this study for realistic evaluation of low-resistivity carbonate reservoirs. Different cementation factors vs. porosity relations have been derived for various carbonate formations worldwide. Shell formula demonstrates that cementation factor increases with decreasing porosity while correlation derived by Borai and Rafiee brought out inverse relation among cementation factors with porosity in tight carbonates and is providing almost constant cementation factor beyond 0.2. But, in our study, a core porosity-cementation factor plot of reservoir facies is showing that below 0.1, m values are increasing with increase of porosity, which is contradictory to Shell formula. This trend of cementation factor at low porosities is due to the presence of secondary porosity. In the porosity range 0.1–0.25, cementation factor increases eventually with the increase of porosity, but beyond porosity values 0.25, increase in porosity causes decrease of cementation factor. This is due to increasing content of mud-supported matrix, which is overall increasing the total porosity but eventually decreasing cementation in a rock. A new nonlinear correlation has been established between m and porosity for Miocene carbonates of Mumbai offshore area, by incorporating all the factors affecting cementation factor (m). Finally, saturation estimated using variable m either using newly established core derived correlation or resistivity image data is giving representative and improved saturation against low-resistivity reservoir layers compared with constant m.

Gas detection technique in low resistivity pays of Lower Acacus reservoir, concession NC7A, Ghadames Basin, NW Libya

Lower Acacus Member is one of the main reservoirs in the Ghadames Basin and it is composed of alternation sandstone and shale facies. It is characterized by low resistivity multiple stacked pays, making the hydrocarbon detection task difficult. To date, no real procedure has been found to correctly define the hydrocarbon in low resistivity intervals. Gas layers as an example could be defined using density and neutron cross-over, but in shaly sand reservoirs as in this case, the shale effect on the neutron log measurement is opposite to the gas effect making the detection of the gas zone by cross-over not valid. On the other hand, the sonic log is affected by the gas by decreasing the rock density, which leads to overestimation of porosity. This phenomenon shall help diagnose the gas presence in low resistivity reservoir. Using color editor of petrophysical software enables the subdivision of log curves into horizontal intervals with specific color or shade based on certain values. Intervals with DT (90-100 msec/ft), which corresponding to 25-36% porosity unit, are shaded red. Comparing production test results with high porosity zones (red shaded on logs) proved that they are gas producers. The advantage of this procedure is that it does not need any extra measurement or calculation; it is only a way of displaying the logs. It has been applied to different gas wells in Ghadames Basin, and it shows a good result. However, this procedure is not applicable to oil pays.

Natural Source Electromagnetic Component Exploration of Coalbed Methane Reservoirs

As an environmentally friendly and high-calorific natural gas, coalbed methane (CBM) has become one of the world’s most crucial unconventional energy sources. Undoubtedly, it is necessary to conduct in-depth research on reservoir exploration methods to ensure high and stable CBM production in the development stage. However, current methods have disadvantages such as high cost, complex devices, and poor terrain adaptability, and therefore they are unsuitable for reasonable monitoring of CBM reservoirs. In contrast, electromagnetic prospecting methods are increasingly widely employed in the rapid delineation of conductive distributions, contributing a lot to in-situ reservoir interpretation. Furthermore, a natural source Super-Low Frequency electromagnetic component method (i.e., the SLF method for short) has been proposed and applied with high potential in a CBM enrichment area, Qinshui Basin, China. In this paper, this method is thoroughly discussed. The magnetic component responses of the SLF method can be used as the characteristic responses of subsurface layers, and the forward modeling algorithms using the finite element method have been successfully developed and verified. On this basis, the direct depth transformation and one-dimensional nonlinear regularization inversion algorithms of the magnetic component responses are proposed for geo-object interpretation. With the help of the empirical mode decomposition (EMD), an SLF data processing workflow is demonstrated theoretically and practically, which is integrated into a portable instrument. The instrument’s ability to identify the low-resistivity reservoirs and their surrounding rocks has been proved by field survey. The extraction of electromagnetic radiation (EMR) anomalies also helps to refine the reservoir interpretation with higher accuracy. A joint comparative inversion test between the SLF method and the audio-magnetotelluric method (AMT) is also addressed, demonstrating that the SLF method is reliably applicable in the field survey of CBM reservoirs. A preliminary statistical analysis shows that the depth resolution of CBM reservoirs can reach the order of tens of meters. Therefore, the SLF method is expected to become one of the most potential options for in-situ CBM exploration with a cost-effective interpretation capability.

Investigation on mechanism of low-resistivity oil reservoir in Minghuazhen and Guantao Formation of Nanpu Sag

Per the resistivity wireline log interpretation, some reservoirs in Nanpu sag have water saturation larger than 50%, however, per oil production testing, those reservoirs have low water production rate. Via FEI QEMSCAN system and FEI scanning electron microscope, the mineral composition, content, distribution and texture of samples from low-resistivity reservoirs in Minghuazhen and Guantao Formation of Nanpu sag have been analyzed. Results show that, the studied samples all have high total clay content, with average volume percentage being 11.62%–15.43%; The main clay minerals of the studied samples are chlorite (average volume percentage 23–35%), illite-smectite mixed layer (average volume percentage 26–31%), and illite (average volume percentage 23–30%). Additional conductivity and absorption of water molecule by these clay minerals are important reasons for the low resistivity wireline response of the studies reservoirs. Pyrite content of the studied samples is 0.08%–3.99% in average volume percentage, and it is highly enriched in several samples, which will increase the electrical conductivity of rock grains of those samples. The back-scattered electron images indicate that the original inter-grain pore with pore diameter being microns contributes the most porosity. However, the clay minerals which broadly, homogeneously and dispersively distribute in samples, as matrix, or part of rock debris, or cement, have effectively enhanced the cation exchange capability of reservoirs, and have created large amount of nano-pores. These nano pores will prominently increase the bound water saturation of reservoirs.

FACTORS INFLUENCING AND DETERMINING THE NATURE OF THE SATURATION OF LOWRESISTANCE RESERVOIRS BY THE EXAMPLE OF THE SURGUTSKY ARCH FIELD

The relevance of the study is determined by the problem associated with the correct assessment of the saturation of low-resistivity reservoirs in the fields of the Surgut arch. According to the materials of geophysical well surveys (GIS) productive low-resistivity reservoirs are interpreted as water-saturated, due to which they are not used in further well operation. The article evaluates the factors influencing the oil saturation index (OSI) readings at one of the unique fields of Surgutsky arch, as well as testing the methodology capable of solving the problem of determining the true saturation of rocks.

Techno-Economic Analysis of Hydraulic Fracturing As A Solution for Developing Low-Resistivity and Low-Quality Zone on Offshore “RI” Field

Hydrocarbon production in Indonesia is continuously decreasing on a yearly basis, which is in contrast with its increasing level of consumption. Low-quality and low-resistivity reservoir zones are deemed to possess a lot of hydrocarbon potentials, however, little priority has been placed on their development due to their small level of production. The "RI" field that was utilized in this study is a mature offshore field with a reservoir which has a low-quality and low-resistivity zone. This area has been in use for more than thirty years, therefore its rate of oil production has declined. This study aims to review the techno-economic aspects of well stimulation in the form of hydraulic fracturing. And also, to determine the development method that is suitable for low-quality fields. The hydraulic fracturing process was modelled using Fracpro software as input parameters for the reservoir production simulations. The reservoir behavior was simulated using the CMG software to observe the amount of hydrocarbon liable for production in various development scenarios. Three cases were performed on the "RI" field, which was stimulated for ten years of operation. The first case was the instance with the natural flow, while the second implemented hydraulic fracturing at the beginning of production, and the third was the implementation of hydraulic fracturing, which started in the middle of the production period. Then, the three cases are evaluated utilizing a Gross Split scheme, to calculate the economics of the project both from the government and contractor's aspects. The simulation study concluded that fracturing at the beginning of the LRLC zone development is the most profitable. The novelty of this study is the comparison of scenarios for the implementation of hydraulic fracturing methods in fields with low-resistivity and low-quality zone whose economic value is evaluated by the Gross Split scheme.

Reservoirs resistivity correction factor in low resistivity pyritic sandstone reservoirs

The presence of pyrite in sandstone reservoirs will cause a problem known as low resistivity reservoirs case. The impact of pyrite volume in sandstone reservoirs is very important to be determined, especially to conduct its resistivity correction factor (Rcf). This research was done in the laboratory used nine sandstone pseudo-cores with various pyrite contents. Some data parameters such as voltage (V) and current (I) are directly measured and others (resistance R and reservoirs resistivity Rt) are calculated. The resistivity of pseudo-core data is calculated using the combined method between Ohm Law and Wenner. The result of this research shows that pyrite will reduce sandstone reservoirs resistivity exponentially. Pyrite will significantly reduce reservoirs resistivity if its presence > 4% and the Rt needs to be corrected. Plotting data between Rt and pyrite volume number for every Sw line can guide us to build a resistivity correction factor (Rcf). This Rcf will drive us to obtain the original resistivity reservoirs (Rto) from apparent well log resistivity reading (Ra) in case of low resistivity caused by pyrite mineral.