scholarly journals Challenge of Water Saturation in Low Resistivity Cretaceous Reservoirs

2018 ◽  
Vol 1 (1) ◽  

This study presents causes and reasons for lowering resistivity logs in carbonate deposit. Moreover this abstract elucidate methods for relieving of challenge water saturation estimation in cretaceous carbonate deposits with Low Resistivity Pay, in Persian Gulf. Reservoirs in the Cretaceous like Zubair, Buwaib, Shuaiba and Khatiyah formations of Southern fields have been analyzed as low resistivity carbonate. Resistivity responses reach less than 6 and even less than 1 ohm.m. Significant hydrocarbon accumulations are “hidden” these Pay zone, (LRPZ). Experimental analysis shows that reservoirs contain clay-coated grains of Lithocodyum algal and is along with Micrtization, Pyritization of digenetic process are reasons for effect on resistivity response. On the other side Smectite and Kaolinite of main clays types have high CEC and greater impact on lowering resistivity. Lønøy method applied to address pore throat sizes which contain Inter crystalline porosity, Chalky Limestone, Mudstone micro porosity. NMR (Nuclear magnetic resonance and Pulse Neutron-Neutron logs have been used to modify the calculated water saturation of the wells. The study shows that reduced specific resistivity is due to texture change and presence of microscopic porosity. For defining reliable water saturation, Core NMR and Log NMR results have been used. NMR results explain that decreasing of resistivity in pay zone is related to texture and grain size variation not being existence of moved water. Irreducible water for the reservoirs is estimated between 30 to 50 %. Low resistivity zone related to microspores with less than 3 micron. Variable T2 cut off is allows to choice suitable T2 cut off values to differentiate movable from bound fluids adapted for the specific carbonate rock. T2 cut off varies between 45 to 110ms. The proper T2 cut off for these formations are extremely crucial to being able to estimate permeability and water saturation.

2021 ◽  
Author(s):  
Mohammad Reza ◽  
Riezal Arieffiandhany ◽  
Debby Irawan ◽  
S Shofiyuddin ◽  
Darmawan Budi Prihanto

Abstract 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


2021 ◽  
Author(s):  
Wael Fares ◽  
Islam Moustafa ◽  
Ali Al Felasi ◽  
Hocine Khemissa ◽  
Omar Al Mutwali ◽  
...  

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.


2021 ◽  
Author(s):  
Ping Zhang ◽  
◽  
Wael Abdallah ◽  
Gong Li Wang ◽  
Shouxiang Mark Ma ◽  
...  

It is desirable to evaluate the possibility of developing a deeper dielectric permittivity based Sw measurement for various petrophysical applications. The low frequency, (< MHz), resistivity-based method for water saturation (Sw) evaluation is the desired method in the industry due to its deepest depth of investigation (DOI, up to 8 ft). However, the method suffers from higher uncertainty when formation water is very fresh or has mixed salinity. Dielectric permittivity and conductivity dispersion have been used to estimate Sw and salinity. The current dielectric dispersion tools, however, have very shallow DOI due to their high measurement frequency up to GHz, which most likely confines the measurements within the near wellbore mud-filtrate invaded zones. In this study, effective medium-model simulations were conducted to study different electromagnetic (EM) induced-polarization effects and their relationships to rock petrophysical properties. Special attention is placed on the complex conductivity at 2 MHz due to its availability in current logging tools. It is known that the complex dielectric saturation interpretation at the MHz range is quite difficult due to lack of fully understood of physics principles on complex dielectric responses, especially when only single frequency signal is used. Therefore, our study is focused on selected key parameters: water-filled porosity, salinity, and grain shape, and their effects on the modeled formation conductivity and permittivity. To simulate field logs, some of the petrophysical parameters mentioned above are generated randomly within expected ranges. Formation conductivity and permittivity are then calculated using our petrophysical model. The calculated results are then mixed with random noises of 10% to make them more realistic like downhole logs. The synthetic conductivity and permittivity logs are used as inputs in a neural network application to explore possible correlations with water-filled porosity. It is found that while the conductivity and permittivity logs are generated from randomly selected petrophysical parameters, they are highly correlated with water-filled porosity. Furthermore, if new conductivity and permittivity logs are generated with different petrophysical parameters, the correlations defined before can be used to predict water-filled porosity in the new datasets. We also found that for freshwater environments, the conductivity has much lower correlation with water-filled porosity than the one derived from the permittivity. However, the correlations are always improved when both conductivity and permittivity were used. This exercise serves as proof of concept, which opens an opportunity for field data applications. Field logs confirm the findings in the model simulations. Two propagation resistivity logs measured at 2 MHz are processed to calculate formation conductivity and permittivity. Using independently estimated water-filled porosity, a model was trained using a neural network for one of the logs. Excellent correlation between formation conductivity and permittivity and water-filled porosity is observed for the trained model. This neural network- generated model can be used to predict water content from other logs collected from different wells with a coefficient of correlation up to 96%. Best practices are provided on the performance of using conductivity and permittivity to predict water-filled porosity. These include how to effectively train the neural network correlation models, general applications of the trained model for logs from different fields. With the established methodology, deep dielectric-based water saturation in freshwater and mixed salinity environments is obtained for enhanced formation evaluation, well placement, and reservoir saturation monitoring.


2021 ◽  
Author(s):  
Ramsin Eyvazzadeh ◽  
Abdullatif Al-Omair ◽  
Majed Kanfar ◽  
Achong Christon

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.


2020 ◽  
Vol 21 (3) ◽  
pp. 9-18
Author(s):  
Ahmed Abdulwahhab Suhail ◽  
Mohammed H. Hafiz ◽  
Fadhil S. Kadhim

   Petrophysical characterization is the most important stage in reservoir management. The main purpose of this study is to evaluate reservoir properties and lithological identification of Nahr Umar Formation in Nasiriya oil field. The available well logs are (sonic, density, neutron, gamma-ray, SP, and resistivity logs). The petrophysical parameters such as the volume of clay, porosity, permeability, water saturation, were computed and interpreted using IP4.4 software. The lithology prediction of Nahr Umar formation was carried out by sonic -density cross plot technique. Nahr Umar Formation was divided into five units based on well logs interpretation and petrophysical Analysis: Nu-1 to Nu-5. The formation lithology is mainly composed of sandstone interlaminated with shale according to the interpretation of density, sonic, and gamma-ray logs. Interpretation of formation lithology and petrophysical parameters shows that Nu-1 is characterized by low shale content with high porosity and low water saturation whereas Nu-2 and Nu-4 consist mainly of high laminated shale with low porosity and permeability. Nu-3 is high porosity and water saturation and Nu-5 consists mainly of limestone layer that represents the water zone.


2017 ◽  
Vol 36 (3) ◽  
pp. 729-733
Author(s):  
MO Ehigiator ◽  
NC Chigbata

A suite of geophysical wire line logs were run in hole. The wells data were acquired from bottom to top and not top to bottom. Basically, we have the qualitative and the quantitative evaluation techniques.Qualitative means is usually used for identification of the type of lithology and also for the component of the formation. Quantitative is used to estimate numerically, the value of what is in the formation. The logs used for evaluation were: Spontaneous potential logs and the Gamma ray logs. These were used to determine the lithology of the formation. Resistivity logs were run in hole to also determine the water saturation in the formation. The Formation Density and the compensated Neutron logs were run in hole to differentiate the gaseous zone from the oil zone in the Hydrocarbon Formation Ogo1, Ogo2 and Ogo3 from well correlation depicts that the subsurface stratigraphy is that of sand – shale intercalations.  Two prominent hydrocarbon bearing reservoirs (R1and R2), at Depth 1563m and 1642mm respectively were identified. The reservoirs were found to have average porosity of 0.22, water saturation 0.43 and Hydrocarbon saturation of 0.57. The reservoirs have permeability of 1376m, volume of oil in place for reservoir 1 and 2 is 39900m3  and 9647 m3   respectively. More. Well correlations are recommended for proper drilling and well completions. 4D seismic acquisitions should be encouraged for proper view of the formation. http://dx.doi.org/10.4314/njt.v36i3.10


2021 ◽  
pp. 4702-4711
Author(s):  
Asmaa Talal Fadel ◽  
Madhat E. Nasser

     Reservoir characterization requires reliable knowledge of certain fundamental properties of the reservoir. These properties can be defined or at least inferred by log measurements, including porosity, resistivity, volume of shale, lithology, water saturation, and permeability of oil or gas. The current research is an estimate of the reservoir characteristics of Mishrif Formation in Amara Oil Field, particularly well AM-1, in south eastern Iraq. Mishrif Formation (Cenomanin-Early Touronin) is considered as the prime reservoir in Amara Oil Field. The Formation is divided into three reservoir units (MA, MB, MC). The unit MB is divided into two secondary units (MB1, MB2) while the unit MC is also divided into two secondary units (MC1, MC2). Using Geoframe software, the available well log images (sonic, density, neutron, gamma ray, spontaneous potential, and resistivity logs) were digitized and updated. Petrophysical properties, such as porosity, saturation of water, saturation of hydrocarbon, etc. were calculated and explained. The total porosity was measured using the density and neutron log, and then corrected to measure the effective porosity by the volume content of clay. Neutron -density cross-plot showed that Mishrif Formation lithology consists predominantly of limestone. The reservoir water resistivity (Rw) values of the Formation were calculated using Pickett-Plot method.   


2020 ◽  
pp. 2979-2990
Author(s):  
Buraq Adnan Al-Baldawi

The present study includes the evaluation of petrophysical properties and lithological examination in two wells of Asmari Formation in Abu Ghirab oil field (AG-32 and AG-36), Missan governorate, southeastern Iraq. The petrophysical assessment was performed utilizing well logs information to characterize Asmari Formation. The well logs available, such as sonic, density, neutron, gamma ray, SP, and resistivity logs, were converted into computerized data using Neuralog programming. Using Interactive petrophysics software, the environmental corrections and reservoir parameters such as porosity, water saturation, hydrocarbon saturation, volume of bulk water, etc. were analyzed and interpreted. Lithological, mineralogical, and matrix recognition studies were performed using porosity combination cross plots. Petrophysical characteristics were determined and plotted as computer processing interpretation (CPI) using Interactive Petrophysics program. Based on petrophysical properties, Asmari Reservoir in Abu Ghirab oil field is classified into three sub formations: Jeribe/ Euphrates and Kirkuk group which is divided into two zones: upper Kirkuk zone, and Middle-Lower Kirkuk zone. Interpretation of well logs of Asmari Formation indicated a commercial Asmari Formation production with medium oil evidence in some ranges of the formation, especially in the upper Kirkuk zone at well X-1. However, well X-2, especially in the lower part of Jeribe/ Euphrates and Middle-Lower Kirkuk zone indicated low to medium oil evidence. Lithology of Asmari Formation demonstrated a range from massive dolomite in Jeribe/ Euphrates zone to limestone in upper Kirkuk zone and limestone and sandstone in middle-lower Kirkuk zone, whereas mineralogy of the reservoir showed calcite and dolomite with few amounts of anhydrite.


2000 ◽  
Vol 3 (06) ◽  
pp. 509-516 ◽  
Author(s):  
Chanh Cao Minh ◽  
Robert Freedman ◽  
Steve Crary ◽  
Darrel Cannon

Summary The recently introduced measurement of total porosity from nuclear magnetic resonance (NMR) tools can help to identify the hydrocarbon type and to improve the determination of formation total porosity (?t) and water saturation (Swt) in combination with other openhole logs. In shaly formations, porosities are difficult to estimate in the presence of hydrocarbons, especially those for gas and light oils. Water saturations are even more difficult to estimate because critical parameters such as clay cation exchange capacities/unit pore volume (QV), the formation factor (F) and formation water resistivity (Rw) might not be known. The latter quantities are essential inputs into the Waxman-Smits and dual-water model saturation equations. In the typical case of shaly gas-bearing formations, both the total porosity corrected for the gas effect and the gas saturation (Sxgas) in the flushed zone can be derived by combining total NMR porosity (?NMR) and density porosity (?density) Adding resistivity logs such as Rxo) and Rt helps to differentiate between gas and oil. Furthermore, the flushed zone water saturation (Sxot) computed from 1?Sxgas can be used in many ways. One procedure uses Sxot in conjunction with the Rxo saturation equation to determine QV or F. Another technique uses Sxot in conjunction with the saturation point (SP) to estimate QV when Rw is known. Yet, another method estimates QV directly from the NMR short relaxation time part of the T2 distribution and use Sxot in conjunction with SP to estimate Rw. The new interpretation procedure follows the sequential shaly sands approach: first, determine porosity, second, determine shaliness, and, third, determine saturation. The new procedure improves on the classical method by offering new ways to compute QV, F and Rw, The methodology is applied to a number of field examples. Introduction Recently, Freedman et al. have shown how to combine ?NMR and ?density to estimate the gas-corrected total porosity ?t and the flushed zone gas saturation in the density magnetic resonance (DMR) method.1 In this paper we build upon their work and integrate NMR logs with other openhole logs in new ways to improve formation evaluation. For hydrocarbon identification, two simple techniques that combine total NMR porosity, density, shallow and deep resistivity logs are shown in a field example. The techniques are simple enough to give a real-time answer when NMR is logged in combination with the above logs. For water saturation determination, total porosity corrected for the hydrocarbon effect and QV is essential. Classical shale sand log analysis first estimates porosity from the density neutron, then corrects for the hydrocarbon effect using Sxot from an Rxo tool in an iterative loop.2 The DMR method does not require any iteration since the linear forms of the density and NMR response equations provide an exact analytical solution of the flushed zone total porosity and saturation. Sxot can then be used in conjunction with an Rxo tool to compute other petrophysical parameters such as QV or F. On the other hand, quantitative use of the SP log in shale sand log analysis was demonstrated by Smits3 in 1968. Integrating SP with NMR and other openhole logs allows the estimation Rw or QV in a two-step SP inversion procedure.4 Both the above techniques to determine QV are applied to a field example. In a second field example, NMR and SP logs are used to compute varying Rw in a fresh water example using continuous QV estimated directly from the NMR short T2 time distribution. The new interpretation methodology is readily extendable to complex lithology, although a multitools solver approach such as the ELAN™ processing method might be preferred.5 (ELAN is a trademark of Schlumberger.) Quicklook Hydrocarbon Identification Gas identification with the DMR method is unambiguous when the deficit between density porosity and total NMR porosity is large [e.g., 6 pore units (p.u.) or more]. When the deficit is not large (a few p.u.), one is not sure whether light oil is present or some gas remains in the pore space after flushing. Because the DMR results depend on the input (gas or light oil), of hydrocarbon type whereas the shallow resistivity does not (it only sees the water phase), it is possible to determine the hydrocarbon type by simply comparing the DMR results with the Rxo results. Rxo?Rt Method A simple method is to compare the flushed zone water saturation determined by the DMR method with the flushed zone water saturation determined from the Rxo tool. If the two saturations agree (meaning that the DMR gas hypothesis is correct) and the Rt tool indicates hydrocarbon, then the hydrocarbon is gas. If the two saturations disagree (meaning that the DMR gas hypothesis is incorrect) and the Rt tool indicates hydrocarbon, then the hydrocarbon is light oil. In the zones where Swt&lt;0.7 (a given saturation cutoff), hydrocarbon is present, and if Sxot, DMR-gas? Sxot, Rxo, then gas or light oil is present.


2000 ◽  
Vol 40 (1) ◽  
pp. 355
Author(s):  
C.J. Shield

Water saturation (Sw) values calculated from resistivity or induction logs are often higher than those measured from core-derived capillary pressure (Pc) measurements. The core-derived Sw measurements are commonly applied for reservoir simulation in preference to the log-derived Sw calculations. As it is economically and logistically impractical to core every hydrocarbon reservoir, a method of correlating the core-derived Sw to resistivity/induction logs is required. Two-dimensional resistivity modelling is applied to dual laterolog data to ascertain the applicability of this technique.The Griffin and Scindian/Chinook Fields, offshore Western Australia, have been producing hydrocarbons since 1994 from two early-to-middle Cretaceous reservoirs, the clean quartzose sandstones of the Zeepaard Formation and the overlying glauconitic, quartzose sandstones of the Birdrong Formation. Routine and special core analysis of cores recovered from wells intersecting these two reservoirs creates an excellent data set with which to correlate the good quality wireline log data.A strong relationship is noted between the modelled water saturation from resistivity logs, and the irreducible water saturation measured from core capillary pressure data. Correlation between the core-derived permeability and the invasion diameter calculated from the modelled laterolog data is shown to produce a locally applicable means of estimating permeability from the resistivity modelling results.The evaluation of these data from the Griffin and Scindian/Chinook Fields provides a method for reducing appraisal and development well analysis costs, through the closer integration of core and wireline log data at an earlier stage of the field appraisal phase.


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