ANALISIS PETROFISIKA DAN PENYEBAB LOW RESISTIVITY RESERVOIR ZONE BERDASARKAN DATA LOG, SEM, XRD DAN PETROGRAFI PADA LAPANGAN X SUMATERA SELATAN

Hydrocarbon reservoir zone located on Low Resistivity is a typical and hidden oil and gas layer which always wrong in assessing as a water layer due to the complex geological origin and resistivity log limitation in identifying hydrocarbon. Presence of shale in a reservoir will decreasing resistivity value and increasing saturation value, so it can cause the results of the analysis to be pessimistic in the identification of hydrocarbons. In that case need to do analysis to core data on research area in order to know the cause of Low Resistivity on reservoir zone that having a probability of hydrocarbon content. Reservoir zone that has low resistivity value is at depth 1572-1577 mD. In this zone, it has a low resistivity value around 2.7- 4.4 ohm-m, with water saturation value around 47%-74% which causes on Low Resistivity reservoir zone to be between hydrocarbons and water reservoir zone. Then, on this research, Low Resistivity is also caused by Lamination Clay of shale type presence which consists of several types of Clay which can cause reservoir zone is at low resistivity value. This Clay type consist of Kaolite 20%, Illite 4%, and Chlorite 4% minerals as well as the presence of other minerals as proponent of low resistivity that is Quartz 60%, Plagioclase 9% and Calcite 3% as conductive minerals.

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KARAKTERISASI RESERVOAR MELALUI ANALISIS PETROFISIKA BERDASARKAN DATA LOG SUMUR “TRD” FORMASI AIR BENAKAT

Jurnal Geofisika Eksplorasi ◽

10.23960/jge.v4i1.7 ◽

2020 ◽

Vol 4 (1) ◽

pp. 78-93

Author(s):

Beny Chasandra ◽

Ordas Dewanto ◽

Ni Putu Juniari

Keyword(s):

Water Saturation ◽

Research Area ◽

Well Log ◽

Petrophysical Analysis ◽

Hydrocarbon Reservoir ◽

Average Porosity ◽

Data Log ◽

Productive Zone

The research area was located in South Sumatra Basin on Air Benakat Formation at South-East Jambi Province. The research conducted to know productive the interest zone by petrophysics analysis (volume shale water saturation, and porosity) and its characteristics by well-log. The lithology of TRD Well is sandstone with a few foraminifera. The interpretation based on the petrophysical analysis porosity of the 7th zone on TRD-10 is average 12,4%, saturation water 19,4% and volume shale 6,2%; the 7th zone on TRD-11 well is average porosity 16,2%, saturation water 41,3%, and volume shale 22%; the 11th zone on TRD-14 well is average porosity 33,2%, saturation water 21,2% and volume shale 1,2%; The 6th zone TRD-15 well, porosity 7,02%, saturation water 32,3% and volume shale 5,6%; On the TRD-17 well of the 7th zone is average the porosity 9,04%, saturation water 25,6% and volume shale 4,6%; and 4th zone of TRD-19 well, porosity 23,2% Saturation water 13,5% and volume shale 7,1%. The characteristics of hydrocarbon reservoir on TRD Wells have low water saturation is less than 50%, porosity more than 5% and volume shale less than 25%. From the result of petrophysics parameter value used as the indicator of the productive zone and interpreted that sand reservoir on well TRD has potentially for the reservoir zone with gas prospect.

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Low Resistivity Reservoir Pay Evaluation, New Opportunity for Further Development, Case Study On Gumai Formation Of B Field, Jambi Sub Basin, South Sumatera Basin

10.29118/ipa21-se-128 ◽

2021 ◽

Keyword(s):

Oil And Gas ◽

Water Saturation ◽

Neutron Density ◽

Gas Field ◽

Data Set ◽

Volume Calculation ◽

Low Resistivity ◽

Shale Volume ◽

Fluid Saturation ◽

Further Development

The understanding of low resistivity reservoir zone is one of the most challenging cases for further development in order to optimize the remaining oil and gas field productions. In the Intra-Gumai Formation “B” Field where marine clastic reservoirs are deposited, a low resistivity reservoir is being developed as a new perforation and workover target. This study discusses how to identify the cause of low resistivity case and evaluate the proper petrophysical parameters to unlock the potential reservoir pay zones. The data set consists of petrographic, X-Ray Diffraction (XRD), Cation Exchange Capacity (CEC), routine core, Drill Stem Test ((DST) and wireline logs data. Petrographic, XRD, CEC and routine analysis were performed to recognize the low resistivity causes characterized by the presence of framework grain (quartz, K-feldspar and glaucony, calcite and kaolinite) observed in intergranular pore and also quartz overgrowth developed prior to kaolinite precipitation. Petrophysical analysis defines the reservoir property parameters by comparing some equations also validated with routine core and DST result. Based on the quantitative analysis carried out, namely the evaluation of the distribution of shale volume, calculation of porosity, and determination of water saturation, it is recommended to use the Stieber method for the distribution of shale volume in the reservoir and its properties, the neutron density porosity method to calculate porosity model, and the Waxman Smits method to determine the final fluid saturation model. Finally, by using the hydrocarbon saturation results in the current study, this interval was improved as pay zone. This method will be applied to other wells and other structures that have a similar depositional environment to increase hydrocarbon reserves in the same field.

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Logging Characteristics and Identification Methods of Low Resistivity Oil Layer: Upper Cretaceous of the Third Member of Qingshankou Formation, Daqingzijing Area, Songliao Basin, China

Geofluids ◽

10.1155/2017/2915646 ◽

2017 ◽

Vol 2017 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Congjun Feng ◽

Murray Gingras ◽

Mengsi Sun ◽

Bing Wang

Keyword(s):

Water Saturation ◽

Thick Layer ◽

Water Layer ◽

High Amplitude ◽

Production Test ◽

Tight Sandstone ◽

Low Resistivity ◽

The Third ◽

Irreducible Water Saturation ◽

Low Amplitude

This study focuses on low resistivity thick layer sandstone in the X~XII groups of the third member of Qingshankou Formation at Daqingzijing oilfield, along with comprehensive data of logging, core, oil test, and production test. Based on the current data, we characterized the logs of low resistivity thick-layer sandstone, quantitatively identified calcareous sandstone and low resistivity reservoir, predicted the reservoir thickness, and further explored the causes of low resistivity reservoir of the region. The resistivity of thick layer sandstone in the X~XII groups of Qingshankou Formation can be classified into low amplitude logfacies, middle amplitude logfacies, and sharp high amplitude logfacies. Sharp high amplitude logfacies sandstone is the tight sandstone of the calcareous cementation. Low amplitude logfacies sandstone is water layer. For the middle amplitude logfacies sandstone, water layer or oil-water layer can be identified with the identification standard. Low amplitude structure, high clay content, high irreducible water saturation, and high formation water salinity are attributed to the origin of low resistivity oil layer.

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Sandstone reservoir characteristics of Rio Del Rey basin, Cameroon, using well-logging analysis

Journal of Petroleum Exploration and Production Technology ◽

10.1007/s13202-021-01211-4 ◽

2021 ◽

Author(s):

Janvier Domra Kana ◽

Ahmad Diab Ahmad ◽

Daniel Hervé Gouet ◽

Xavier Djimhoudouel ◽

Serge Parfait Koah Na Lebogo

Keyword(s):

Oil And Gas ◽

Gamma Ray ◽

Water Saturation ◽

Effective Porosity ◽

High Resistivity ◽

Neutron Density ◽

Oil And Gas Exploration ◽

Hydrocarbon Reservoir ◽

Offshore Oil And Gas ◽

Cloud Of Points

AbstractThe present work deals with an interpretation of well log data (gamma ray (GR), resistivity, density, and neutron) from four wells, namely P-1, P-2, P-3 and P-4 in the study area of the Rio Del Rey basin. The well logs analysis indicates five potential sandstone reservoirs at the P-1, two at the P-2, four at the P-3 and six at the P-4. The neutron–density-GR logs highlight the sandstone gas reservoir characterized by high resistivity and crossover between neutron density. The neutron–density-GR cross-plot confirms the presence of sandstone containing hydrocarbons by a displacement of the cloud of points, from low to medium GR values, from the sandstone line to the left. Petrophysical parameters exhibit the value 12–41% for a volume of shale, 15–34% for effective porosity, 29–278 mD for permeability and 3–63% for water saturation. The three potential hydrocarbon reservoir saturation ranges from 22 to 45%. The study will contribute to future offshore oil and gas exploration and development in the Rio Del Rey basin, based on the geological and geophysical characteristics of the reservoirs delineated.

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Using the Modified Resistivity–Porosity Cross Plot Method to Identify Formation Fluid Types in Tight Sandstone with Variable Water Salinity

Energies ◽

10.3390/en14196335 ◽

2021 ◽

Vol 14 (19) ◽

pp. 6335

Author(s):

Yufei Yang ◽

Kesai Li ◽

Yuanyuan Wang ◽

Hucheng Deng ◽

Jianhua He ◽

...

Keyword(s):

Water Saturation ◽

Ordos Basin ◽

Water Layer ◽

Water Salinity ◽

Water Type ◽

Formation Water ◽

Tight Sandstone ◽

Fluid Identification ◽

Low Resistivity ◽

Formation Water Salinity

It is generally difficult to identify fluid types in low-porosity and low-permeability reservoirs, and the Chang 8 Member in the Ordos Basin is a typical example. In the Chang 8 Member of Yanchang Formation in the Zhenyuan area of Ordos Basin, affected by lithology and physical properties, the resistivity of the oil layer and water layer are close, which brings great difficulties to fluid type identification. In this paper, we first analyzed the geological and petrophysical characteristics of the study area, and found that high clay content is one of the reasons for the low-resistivity oil pay layer. Then, the formation water types and characteristics of formation water salinity were studied. The water type was mainly CaCl2, and formation water salinity had a great difference in the study area ranging from 7510 ppm to 72,590 ppm, which is the main cause of the low-resistivity oil pay layer. According to the reservoir fluid logging response characteristics, the water saturation boundary of the oil layer, oil–water layer and water layer were determined to be 30%, 65% and 80%, respectively. We modified the traditional resistivity–porosity cross plot method based on Archie’s equations, and established three basic plates with variable formation water salinity, respectively. The above method was used to identify the fluid types of the reservoirs, and the application results indicate that the modified method agrees well with the perforation test data, which can effectively improve the accuracy of fluid identification. The accuracy of the plate is 88.1%. The findings of this study can help for a better understanding of fluid identification and formation evaluation.

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Understanding the Flow Dynamics of CO2 Plumes in the Subsurface

10.2118/207539-ms ◽

2021 ◽

Author(s):

Florence Letitia Bebb ◽

Kate Clare Serena Evans ◽

Jagannath Mukherjee ◽

Bilal Saeed ◽

Geovani Christopher

Keyword(s):

Carbon Capture ◽

Oil And Gas ◽

Co2 Storage ◽

Flow Dynamics ◽

Hydrocarbon Exploration ◽

Hydrocarbon Reservoir ◽

Exploration And Production ◽

And Migration ◽

Carbon Dioxide Co2 ◽

Hydrocarbon Exploration And Production

Abstract There are several significant differences between the behavior of injected CO2 and reservoired hydrocarbons in the subsurface. These fundamental differences greatly influence the modeling of CO2 plumes. Carbon capture, utilization, and storage (CCUS) is growing in importance in the exploration and production (E&P) regulatory environment with the Oil and Gas Climate Initiative (OGCI) making CCUS a priority. Companies need to prospect for storage sites and evaluate both the short-term risks and long-term fate of stored carbon dioxide (CO2). Understanding the physics governing fluid flow is important to both CO2 storage and hydrocarbon exploration and production. In the last decade, there has been much research into the movement and migration of CO2 in the subsurface. A better understanding of the flow dynamics of CO2 plumes in the subsurface has highlighted a number of significant differences in modeling CO2 storage sites compared with hydrocarbon reservoir simulations. These differences can greatly influence reliability when modeling CO2 storage sites.

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An Advanced Ultra-Deep Resistivity Mapping Sensor Reduced Reservoir Uncertainty and Eliminated the Need for a Pilot Hole for the First Time; A Case Study from Offshore Abu Dhabi

10.2118/204813-ms ◽

2021 ◽

Author(s):

Wael Fares ◽

Islam Moustafa ◽

Ali Al Felasi ◽

Hocine Khemissa ◽

Omar Al Mutwali ◽

...

Keyword(s):

Real Time ◽

Water Saturation ◽

Firing Frequency ◽

Target Zones ◽

The Real ◽

Pilot Hole ◽

Low Resistivity ◽

Milling Operations ◽

The Future ◽

Identify Formation

Abstract The high reservoir uncertainty, due to the lateral distribution of fluids, results in variable water saturation, which is very challenging in drilling horizontal wells. In order to reduce uncertainty, the plan was to drill a pilot hole to evaluate the target zones and plan horizontal sections based on the information gained. To investigate the possibility of avoiding pilot holes in the future, an advanced ultra-deep resistivity mapping sensor was deployed to map the mature reservoirs, to identify formation and fluid boundaries early before penetrating them, avoiding the need for pilot holes. Prewell inversion modeling was conducted to optimize the spacing and firing frequency selection and to facilitate an early real-time geostopping decision. The plan was to run the ultra-deep resistivity mapping sensor in conjunction with shallow propagation resistivity, density, and neutron porosity tools while drilling the 8 ½-in. landing section. The real-time ultra-deep resistivity mapping inversion was run using a depth of inversion up to 120 ft., to be able to detect the reservoir early and evaluate the predicted reservoir resistivity. This would allow optimization of any geostopping decision. The ultra-deep resistivity mapping sensor delivered accurate mapping of low resistivity zones up to 85 ft. TVD away from the wellbore in a challenging low resistivity environment. The real-time ultra-deep resistivity mapping inversion enabled the prediction of resistivity values in target zones prior to entering the reservoir; values which were later crosschecked against open-hole logs for validation. The results enabled identification of the optimal geostopping point in the 8 ½-in. section, enabling up to seven rig days to be saved in the future by eliminating a pilot hole. In addition this would eliminate the risk of setting a whipstock at high inclination with the subsequent impact on milling operations. In specific cases, this minimizes drilling risks in unknown/high reservoir pressure zones by improving early detection of formation tops. Plans were modified for a nearby future well and the pilot-hole phase was eliminated because of the confidence provided by these results. Deployment of the ultra-deep resistivity mapping sensor in these mature carbonate reservoirs may reduce the uncertainty associated with fluid migration. In addition, use of the tool can facilitate precise geosteering to maintain distance from fluid boundaries in thick reservoirs. Furthermore, due to the depths of investigation possible with these tools, it will help enable the mapping of nearby reservoirs for future development. Further multi-disciplinary studies remain desirable using existing standard log data to validate the effectiveness of this concept for different fields and reservoirs.

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Low Resistivity Pay Carbonates: A Practical Approach to Quantify Water Saturation Using a Modified Archie's Model

10.2118/206096-ms ◽

2021 ◽

Author(s):

Ramsin Eyvazzadeh ◽

Abdullatif Al-Omair ◽

Majed Kanfar ◽

Achong Christon

Keyword(s):

Machine Learning ◽

Water Saturation ◽

Pore Space ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Theoretical Research ◽

Low Contrast ◽

Practical Applications ◽

Low Resistivity ◽

New Models

Abstract A detailed description of a modified Archie's equation is proposed to accurately quantify water saturation within low resistivity/low contrast pay carbonates. The majority of previous work on low resistivity/low contrast reservoirs focused on clastics, namely, thin beds and/or clay effects on resistivity measurements. Recent publications have highlighted a "non-Archie" behavior in carbonates with complex pore structures. Several theoretical models were introduced, but new practical applications were not derived to solve this issue. Built upon previous theoretical research in a holistic approach, new models and workflows have been developed. Specifically, utilizing a combination of machine learning algorithms, nuclear magnetic resonance (NMR), core and geological data, field specific calibrated equations to compute water saturation (Sw) in complex carbonate formations are presented. Essentially, these new models partition the porosity into pore spaces and calculate their relative contribution to water saturation in each pore space. These calibrated equations robustly produce results that have proven invaluable in pay identification, well placement, and have greatly enhanced the ability to manage these types of reservoirs. This paper initially explains the theory behind the development of the analysis illustrating workflows and validation techniques used to qualify this methodology. A key benefit performing this research is the utilization of machine-learning algorithms to predict NMR derived values in wells that do not have NMR data. Several examples explore where results of this analysis are compared to dynamic testing, formation testing and laboratory measured samples to validate and demonstrate the utility of this new analysis.

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Experimental Study on the Saturation Model of Volcanic Rock Based on Fluid Distribution

Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description ◽

10.30632/pjv62n4-2021a6 ◽

2021 ◽

Vol 62 (4) ◽

pp. 422-433

Author(s):

Baozhi Pan ◽

◽

Weiyi Zhou ◽

Yuhang Guo ◽

Zhaowei Si ◽

...

Keyword(s):

Volcanic Rock ◽

Oil And Gas ◽

Water Saturation ◽

Early Stage ◽

Evaluation Model ◽

Acoustic Velocity ◽

Distribution Model ◽

Fluid Distribution ◽

Water Drainage ◽

Gas Reservoirs

A saturation evaluation model suitable for Nanpu volcanic rock formation is established based on the experiment of acoustic velocity changing with saturation during the water drainage process of volcanic rock in the Nanpu area. The experimental data show that in the early stage of water drainage, the fluid distribution in the pores of rock samples satisfies the patchy formula. With the decrease of the sample saturation, the fluid distribution in the pores is more similar to the uniform fluid distribution model. In this paper, combined with the Gassmann-Brie and patchy formula, the calculation equation of Gassmann-Brie-Patchy (G-B-P) saturation is established, and the effect of contact softening is considered. The model can be used to calculate water saturation based on acoustic velocity, which provides a new idea for the quantitative evaluation of volcanic oil and gas reservoirs using seismic and acoustic logging data.

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Identification of Oil Reservoir Meeting Atypical Archie Phenomenon

The Open Petroleum Engineering Journal ◽

10.2174/1874834101508010354 ◽

2015 ◽

Vol 8 (1) ◽

pp. 354-357

Author(s):

Shixiong Yuan ◽

Haimin Guo ◽

Yu Ding ◽

Rui Deng

Keyword(s):

Pore Structure ◽

Water Saturation ◽

Saturation Index ◽

Oil Reservoir ◽

Formation Water ◽

Core Data ◽

Hydrocarbon Reservoir ◽

Pore Structures ◽

Structure Index ◽

Very High

According to core data, this paper studies variation of resistivity in different pore structures and wettability conditions. The results show that with the increase of pore structure index m, the resistivity will increase significantly when the saturation is constant. Similarly, with increasing saturation index n, the resistivity will also increase even with the same saturation. With fixed m and n, the calculated formation water saturation will be very high, resulting in hydrocarbon reservoir being ignored. This variation characteristic is significant for the identification of hidden reservoir with atypical Archie formula.

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