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

2021 ◽  
Author(s):  
Mohammad Reza ◽  
Riezal Arieffiandhany ◽  
Debby Irawan ◽  
S Shofiyuddin ◽  
Darmawan Budi Prihanto
Keyword(s):  
High Resolution ◽  
Transition Zone ◽  
Water Saturation ◽  
Electrical Parameter ◽  
Parameter Determination ◽  
Type Curve ◽  
Petrophysical Analysis ◽  
Low Resistivity ◽  
Resistivity Logs ◽  
Shaly Sand

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

scholarly journals Challenge of Water Saturation in Low Resistivity Cretaceous Reservoirs

2018 ◽  
Vol 1 (1) ◽  
Keyword(s):  
Water Saturation ◽  
Size Variation ◽  
Pulse Neutron ◽  
Specific Resistivity ◽  
Pore Throat ◽  
Low Resistivity ◽  
Texture Change ◽  
Carbonate Deposit ◽  
Resistivity Logs ◽  
Carbonate Deposits

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.

scholarly journals Petrophysical Analysis of ‘Bright’ Field, Onshore Niger Delta

2021 ◽  
Keyword(s):  
Niger Delta ◽  
Gamma Ray ◽  
Water Saturation ◽  
Research Work ◽  
Accurate Estimation ◽  
Bright Field ◽  
Petrophysical Analysis ◽  
Lithology Identification ◽  
Resistivity Logs ◽  
Sand Bodies

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.

Formation-Resistivity Theory: How Archie Equations, Shaly-Reservoir Models, Conductive Rock-Matrix Model, and Dual-Triple-Porosity Models Are Related

2014 ◽  
Vol 17 (02) ◽  
pp. 141-151 ◽  
Author(s):  
Philip C. Iheanacho
Keyword(s):  
Water Saturation ◽  
Control Volume ◽  
Single Parameter ◽  
Rock Matrix ◽  
Heterogeneous Reservoirs ◽  
Resistivity Logs ◽  
Resistivity Model ◽  
Formation Resistivity ◽  
Shaly Sand ◽  
New Equation

Summary The estimation of hydrocarbon pore volume (HCPV) from resistivity logs can be quite troublesome in some complex heterogeneous reservoirs. Most water-saturation/formation-resistivity models that work well for some reservoirs give unreliable results for others. No single model works for all types of reservoir scenarios. This paper presents the theory of formation resistivity in porous media. The paper develops the theory from the parallel-resistivity model and then extends it for the series-resistivity model. When applied for clean sand, the theory derives Archie equations from the first principle. The derivations show that both porosity exponent and saturation exponent are of the same origin and should have the same name. A better name for both parameters should be the tortuosity exponent of a component with respect to its fraction in a control volume. It is also advantageous to treat as a single parameter rather than two separate parameters. In addition, this theory derives new shaly-sand models for estimating HCPV. These new shaly-sand models can be used for different types of shale distribution by adjusting the value of a single parameter in the models. The formation-resistivity theory is also used to derive formation-resistivity models for conductive rock-matrix reservoirs and dual-triple-porosity reservoirs. A new equation for calculating the composite-porosity exponent is also developed. Field data are used to validate this work. The theory, when applied for each scenario, derives formation-resistivity models for estimating the reliable HCPV of different reservoir scenarios and types. Moreover, the strength of this theory is its ability to generate models that closely resemble models that have proved to work well for the reservoir cases for which they were developed. Although this work does not test the theory for the cases of tight-sand, shale-gas, and other unconventional reservoirs because of the unavailability of such data, the author is of the opinion that the theory can easily be extended for such reservoirs if the necessary data are available.

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

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.

ENHANCED ASSESSMENT OF FLUID SATURATION IN THE WOLFCAMP FORMATION OF THE PERMIAN BASIN

2021 ◽  
Author(s):  
Sabyasachi Dash ◽  
◽  
Zoya Heidari ◽  
Keyword(s):  
Water Saturation ◽  
Geometric Model ◽  
Rock Physics ◽  
Model Parameters ◽  
Permian Basin ◽  
Resistivity Measurements ◽  
Clay Concentration ◽  
Resistivity Model ◽  
Shaly Sand ◽  
Hydrocarbon Reserves

Conventional resistivity models often overestimate water saturation in organic-rich mudrocks and require extensive calibration efforts. Conventional resistivity-porosity-saturation models assume brine in the formation as the only conductive component contributing to resistivity measurements. Enhanced resistivity models for shaly-sand analysis include clay concentration and clay-bound water as contributors to electrical conductivity. These shaly-sand models, however, consider the existing clay in the rock as dispersed, laminated, or structural, which does not reliably describe the distribution of clay network in organic-rich mudrocks. They also do not incorporate other conductive minerals and organic matter, which can significantly impact the resistivity measurements and lead to uncertainty in water saturation assessment. We recently introduced a method that quantitatively assimilates the type and spatial distribution of all conductive components to improve reserves evaluation in organic-rich mudrocks using electrical resistivity measurements. This paper aims to verify the reliability of the introduced method for the assessment of water/hydrocarbon saturation in the Wolfcamp formation of the Permian Basin. Our recently introduced resistivity model uses pore combination modeling to incorporate conductive (clay, pyrite, kerogen, brine) and non-conductive (grains, hydrocarbon) components in estimating effective resistivity. The inputs to the model are volumetric concentrations of minerals, the conductivity of rock components, and porosity obtained from laboratory measurements or interpretation of well logs. Geometric model parameters are also critical inputs to the model. To simultaneously estimate the geometric model parameters and water saturation, we develop two inversion algorithms (a) to estimate the geometric model parameters as inputs to the new resistivity model and (b) to estimate the water saturation. Rock type, pore structure, and spatial distribution of rock components affect geometric model parameters. Therefore, dividing the formation into reliable petrophysical zones is an essential step in this method. The geometric model parameters are determined for each rock type by minimizing the difference between the measured resistivity and the resistivity, estimated from Pore Combination Modeling. We applied the new rock physics model to two wells drilled in the Permian Basin. The depth interval of interest was located in the Wolfcamp formation. The rock-class-based inversion showed variation in geometric model parameters, which improved the assessment of water saturation. Results demonstrated that the new method improved water saturation estimates by 32.1% and 36.2% compared to Waxman-Smits and Archie's models, respectively, in the Wolfcamp formation. The most considerable improvement was observed in the Middle and Lower Wolfcamp formation, where the average clay concentration was relatively higher than the other zones. Results demonstrated that the proposed method was shown to improve the estimates of hydrocarbon reserves in the Permian Basin by 33%. The hydrocarbon reserves were underestimated by an average of 70000 bbl/acre when water saturation was quantified using Archie's model in the Permian Basin. It should be highlighted that the new method did not require any calibration effort to obtain model parameters for estimating water saturation. This method minimizes the need for extensive calibration efforts for the assessment of hydrocarbon/water saturation in organic-rich mudrocks. By minimizing the need for extensive calibration work, we can reduce the number of core samples acquired. This is the unique contribution of this rock-physics-based workflow.

DEEP DIELECTRIC-BASED WATER SATURATION IN FRESHWATER AND MIXED SALINITY ENVIRONMENTS

2021 ◽  
Author(s):  
Ping Zhang ◽  
◽  
Wael Abdallah ◽  
Gong Li Wang ◽  
Shouxiang Mark Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Dielectric Permittivity ◽  
Water Saturation ◽  
Low Frequency ◽  
Single Frequency ◽  
Model Simulations ◽  
Petrophysical Parameters ◽  
Effective Medium Model ◽  
Correlation Models ◽  
Resistivity Logs

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.

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

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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