scholarly journals Geophysical reservoir monitoring feasibility study in a Central Saudi Arabian oil field

GeoArabia ◽  
2006 ◽  
Vol 11 (4) ◽  
pp. 63-72
Author(s):  
Aldo L. Vesnaver ◽  
Michael K. Broadhead ◽  
Isidore J. Bellaci
Keyword(s):  
Reservoir Characterization ◽  
Water Saturation ◽  
Time Lapse ◽  
Oil Field ◽  
Oil Fields ◽  
Reservoir Monitoring ◽  
Porosity And Permeability ◽  
Low Probability ◽  
Potential Impact ◽  
Productive Zone

ABSTRACT The Central Arabian field of this study is part of a trend of oil fields primarily producing from Permian sandstone reservoirs. The most productive zone, in the upper part of the reservoir, is characterized with good porosity and permeability, an aeolian depositional environment, and producing zones that tend to be laterally and vertically heterogeneous. The reservoir sandstone lenses are interspersed with low porosity/permeability siltstones. We examined the feasibility of watersaturation surveillance by geophysical means that could help to better produce the field and unravel certain production challenges; hence, time-lapse seismic (4-D) was considered. Using modeling, we argue that time-lapse seismic is a low probability candidate for successful reservoir monitoring of water saturation in this field. We also discuss other techniques that are potential alternatives, such as micro-seismicity, magnetotellurics and borehole gravity, comparing the relative merits and limitations of these methods as applicable to this field. Finally, we conclude with the potential impact of improved reservoir characterization, via integration of more seismic information into the reservoir model.

scholarly journals Estimating reservoir permeability with borehole radar

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. H51-H60
Author(s):  
Feng Zhou ◽  
Iraklis Giannakis ◽  
Antonios Giannopoulos ◽  
Klaus Holliger ◽  
Evert Slob
Keyword(s):  
Water Saturation ◽  
Time Lapse ◽  
Drilling Mud ◽  
Invasion Depth ◽  
Initial Water ◽  
Oil Drilling ◽  
Reservoir Evaluation ◽  
Reservoir Permeability ◽  
Borehole Radar ◽  
Porosity And Permeability

In oil drilling, mud filtrate penetrates into porous formations and alters the compositions and properties of the pore fluids. This disturbs the logging signals and brings errors to reservoir evaluation. Drilling and logging engineers therefore deem mud invasion as undesired and attempt to eliminate its adverse effects. However, the mud-contaminated formation carries valuable information, notably with regard to its hydraulic properties. Typically, the invasion depth critically depends on the formation porosity and permeability. Therefore, if adequately characterized, mud invasion effects could be used for reservoir evaluation. To pursue this objective, we have applied borehole radar to measure mud invasion depth considering its high radial spatial resolution compared with conventional logging tools, which then allows us to estimate the reservoir permeability based on the acquired invasion depth. We investigate the feasibility of this strategy numerically through coupled electromagnetic and fluid modeling in an oil-bearing layer drilled using freshwater-based mud. Time-lapse logging is simulated to extract the signals reflected from the invasion front, and a dual-offset downhole antenna mode enables time-to-depth conversion to determine the invasion depth. Based on drilling, coring, and logging data, a quantitative interpretation chart is established, mapping the porosity, permeability, and initial water saturation into the invasion depth. The estimated permeability is in a good agreement with the actual formation permeability. Our results therefore suggest that borehole radar has significant potential to estimate permeability through mud invasion effects.

scholarly journals An Integrated Microfacies and Well Logs-Based Reservoir Characterization of Yamama Formation, Southern Iraq

2021 ◽  
pp. 3570-3586
Author(s):  
Mohanad M. Al-Ghuribawi ◽  
Rasha F. Faisal
Keyword(s):  
Reservoir Characterization ◽  
Water Saturation ◽  
Effective Porosity ◽  
Well Logs ◽  
Petrophysical Properties ◽  
Reservoir Properties ◽  
Thin Sections ◽  
Porosity And Permeability ◽  
South Of Iraq ◽  
Southern Iraq

     The Yamama Formation includes important carbonates reservoir that belongs to the Lower Cretaceous sequence in Southern Iraq. This study covers two oil fields (Sindbad and Siba) that are distributed Southeastern Basrah Governorate, South of Iraq. Yamama reservoir units were determined based on the study of cores, well logs, and petrographic examination of thin sections that required a detailed integration of geological data and petrophysical properties. These parameters were integrated in order to divide the Yamama Formation into six reservoir units (YA0, YA1, YA2, YB1, YB2 and YC), located between five cap rock units. The best facies association and petrophysical properties were found in the shoal environment, where the most common porosity types were the primary (interparticle) and secondary (moldic and vugs) . The main diagenetic process that occurred in YA0, YA2, and YB1 is cementation, which led to the filling of pore spaces by cement and subsequently decreased the reservoir quality (porosity and permeability). Based on the results of the final digital  computer interpretation and processing (CPI) performed by using the Techlog software, the units YA1 and YB2 have the best reservoir properties. The unit YB2 is characterized by a good effective porosity average, low water saturation, good permeability, and large thickness that distinguish it from other reservoir units.

scholarly journals Petrophysical Properties of Nahr Umar Formation in Nasiriya Oil Field

2020 ◽  
Vol 21 (3) ◽  
pp. 9-18
Author(s):  
Ahmed Abdulwahhab Suhail ◽  
Mohammed H. Hafiz ◽  
Fadhil S. Kadhim
Keyword(s):  
Gamma Ray ◽  
Water Saturation ◽  
Oil Field ◽  
Well Logs ◽  
High Porosity ◽  
Reservoir Properties ◽  
Petrophysical Parameters ◽  
Resistivity Logs ◽  
Lithology Prediction ◽  
Porosity And Permeability

   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.

scholarly journals Determination of Shale Volume, Shale Types and Effective Porosity Based On Cross Plotting

2018 ◽  
Vol 1 (1) ◽  
Keyword(s):  
Great Degree ◽  
Effective Porosity ◽  
Oil Field ◽  
Oil Fields ◽  
Well Log ◽  
Petrophysical Parameters ◽  
Shale Volume ◽  
Kangan Formation ◽  
Porosity And Permeability

The evaluation of shaley formations has long been a difficult task. Presence of shale and shale types in some of the Iranian formations are one of the most important factors. Shale types have to be considered, because existence of shale reduces, porosity and permeability of the reservoir to some degree. Shale Distributed in formations in three basic types, Dispersed, Laminar and structural. Each of these shale types has different effect on porosity, permeability and saturation. Dispersed shale reduces porosity and permeability to a great degree, but, laminar shale and structural shale have little effect on petrophysical parameters. In this investigation, shale types, Shale volume and effective porosity of Kangan Formation have been determined from well log data and compared with crossplotting. In other words, a triangle Density-Neutron cross-plot is used to determine above parameters. The area of study lies in central oil fields of Iran, where one of the well is used (Tabnak Well C). Tabnak Well C selected to study Kangan Formation from Iranian oil field, in Pars onshore. This study illustrates that distribution of shale types in Kangan Formation is mainly dispersed shale with few of laminar shale, and percentage of effective porosity (φe) decreases with increasing depths for Kangan Formation.

scholarly journals Characterization of Mishrif Formation Reservoir in Amara Oil Field, Southeast Iraq, Using Geophysical Well-logging

2021 ◽  
pp. 4702-4711
Author(s):  
Asmaa Talal Fadel ◽  
Madhat E. Nasser
Keyword(s):  
Reservoir Characterization ◽  
Gamma Ray ◽  
Water Saturation ◽  
Total Porosity ◽  
Effective Porosity ◽  
Oil Field ◽  
Neutron Density ◽  
Reservoir Water ◽  
Resistivity Logs ◽  
Reliable Knowledge

     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.   

scholarly journals Permeability Estimation of Khasib Formation in Amara Oil Field from Well logs Data Using Multilinear Regression Technique and Empirical

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Buraq Adnan. Al-Baldawi
Keyword(s):  
Water Saturation ◽  
Statistical Correlation ◽  
Oil Field ◽  
Regression Technique ◽  
Well Logs ◽  
Multilinear Regression ◽  
Irreducible Water Saturation ◽  
Porosity And Permeability ◽  
Empirical Approaches ◽  
Interconnected Pores

Permeability is the property that permits the passage of fluids through the interconnected pores of a rock. It is one of the most important, most spatially variable, most uncertain, and  hence  least  predictable transport properties of porous formations. This paper represents a method to predict permeability of Khasib Formation in two wells (Am-1,Am-2) of Amara field using Multilinear regression (MLR) technique and various empirical models, such as Tixier’s, Timur’s and Coates and Dumanoir equations, are used to quantify permeability from well log calculations of porosity and irreducible water saturation. Measured porosity and permeability data from plugs of the available core intervals were used for validation of the predicated data from the logs. The calculated permeability values were compared with the laboratory measurements of core samples to those estimated from different empirical approaches, such as Tixier, Timur, Coates and Dumanoir models, as well as multilinear regression technique by using the statistical correlation coefficient (R2). The present study indicates that Multilinear regression (MLR) technique is the best method and the most validity to estimate permeability from well logs data.

scholarly journals Integrated reservoir characterization and quality analysis of the carbonate rock types, case study, southern Iraq

2020 ◽  
Vol 10 (8) ◽  
pp. 3157-3177 ◽  
Author(s):  
Sameer Noori Ali Al-Jawad ◽  
Muhammad Abd Ahmed ◽  
Afrah Hassan Saleh
Keyword(s):  
Reservoir Characterization ◽  
Water Saturation ◽  
Rock Type ◽  
High Water ◽  
Quality Analysis ◽  
Reservoir Quality ◽  
High Porosity ◽  
Rock Types ◽  
Porosity And Permeability ◽  
Medium Porosity

Abstract The reservoir characterization and rock typing is a significant tool in performance and prediction of the reservoirs and understanding reservoir architecture, the present work is reservoir characterization and quality Analysis of Carbonate Rock-Types, Yamama carbonate reservoir within southern Iraq has been chosen. Yamama Formation has been affected by different digenesis processes, which impacted on the reservoir quality, where high positively affected were: dissolution and fractures have been improving porosity and permeability, and destructive affected were cementation and compaction, destroyed the porosity and permeability. Depositional reservoir rock types characterization has been identified depended on thin section analysis, where six main types of microfacies have been recognized were: packstone-grainstone, packstone, wackestone-packstone, wackestone, mudstone-wackestone, and mudstone. By using flow zone indicator, four groups have been defined within Yamama Formation, where the first type (FZI-1) represents the bad quality of the reservoir, the second type (FZI-2) is characterized by the intermediate quality of the reservoir, third type (FZI-3) is characterized by good reservoir quality, and the fourth type (FZI-4) is characterized by good reservoir quality. Six different rock types were identified by using cluster analysis technique, Rock type-1 represents the very good type and characterized by low water Saturation and high porosity, Rock type-2 represents the good rock type and characterized by low water saturation and medium–high porosity, Rock type-3 represents intermediate to good rock type and characterized by low-medium water saturation and medium porosity, Rock type-4 represents the intermediate rock type and characterized by medium water saturation and low–medium porosity, Rock type-5 represents intermediate to bad rock type and characterized by medium–high water saturation and medium–low porosity, and Rock type-6 represents bad rock type and characterized by high water saturation and low porosity. By using Lucia Rock class typing method, three types of rock type classes have been recognized, the first group is Grain-dominated Fabrics—grainstone, which represents a very good rock quality corresponds with (FZI-4) and classified as packstone-grainstone, the second group is Grain-dominated Fabrics—packstone, which corresponds with (FZI-3) and classified as packstone microfacies, the third group is Mud-dominated Fabrics—packstone, packstone, correspond with (FZI-1 and FZI-2) and classified as wackestone, mudstone-wackestone, and mudstone microfacies.

Time‐lapse (4-D) seismic monitoring of primary production of turbidite reservoirs at South Timbalier Block 295, offshore Louisiana, Gulf of Mexico

Geophysics ◽  
2000 ◽  
Vol 65 (2) ◽  
pp. 351-367 ◽  
Author(s):  
Tucker Burkhart ◽  
Andrew R. Hoover ◽  
Peter B. Flemings
Keyword(s):  
Reservoir Characterization ◽  
Water Saturation ◽  
Seismic Analysis ◽  
Time Lapse ◽  
Hydrocarbon Production ◽  
Seismic Surveys ◽  
Seismic Amplitude ◽  
Fluid Properties ◽  
Multiple Reservoir ◽  
Gas Drive

Two seismic surveys acquired over South Timbalier Block 295 field (offshore Louisiana) record significant differences in amplitude that are correlated to hydrocarbon production at multiple reservoir levels. The K8 sand, a solution‐gas‐drive reservoir, shows increases in seismic amplitude associated with gas exsolution. The K40 sand, a water‐drive reservoir, shows decreases in seismic amplitude associated with increases in water saturation. A methodology is presented to optimize the correlation between two seismic surveys after they have been individually processed (poststack) This methodology includes rebinning, crosscorrelation, band‐pass filtering, and cross‐equalization. A statistical approach is developed to characterize the correlation between the seismic surveys. This statistical analysis is used to discriminate seismic amplitude differences that record change in rock and fluid properties from those that could be the result of miscorrelation of the seismic data. Time‐lapse seismic analysis provides an important new approach to imaging hydrocarbon production; it may be used to improve reservoir characterization and guide production decisions.

scholarly journals 3D Geological Modeling for Yamama Reservoir in Subba, Luhias and Ratawi Oil Fields, South of Iraq

2019 ◽  
Vol 60 (5) ◽  
pp. 1023-1036
Author(s):  
Naseem Sh. ALhakeem ◽  
Medhat E. Nasser ◽  
Ghazi H. AL-Sharaa
Keyword(s):  
Water Saturation ◽  
Salt Water ◽  
Oil Field ◽  
Oil Fields ◽  
Geological Model ◽  
Petrophysical Properties ◽  
Geological Modeling ◽  
3D Geological Modeling ◽  
South Of Iraq ◽  
Resistivity Log

3D geological model for each reservoir unit comprising the Yamama Formation revealed to that the formation is composed of alternating reservoirs and barriers. In Subba and Luhais fields the formation began with barrier YB-1 and four more barriers (YB-2, YB-3, YB-4, YB-5), separated five reservoirs (YR-A, YR-B, YR-C, YR-D, YR-E) ranging in thickness from 70 to 80 m for each of them deposited by five sedimentary cycles. In the Ratawi field the formation was divided into three reservoir units (YR-A, YR-B, and YR-C) separated by two barrier units (YB-2 and YB-3), the first cycle is missing in Ratawi field.   The study involves 1 well in Luhais field (Lu-12), 3 wells in Subba field (Su-7, Su-8, and Su-9), and 5 wells in Ratawi field (Rt-3, Rt-4, Rt-5, Rt-6 and Rt-7), the Luhais, Subba, and Ratawi fields located in the Mesopotamia zone (Zubair subzone). The reservoir units (YR-C and YR-D) in Subba oil field, and YR-B in Ratawi oil field represent the major reservoir units that characterized by the best Petrophysical properties (the highest porosity, the lowest water saturation, and the best Net Pay Thickness), Luhais oil field has poor to moderate Petrophysical properties and low oil bearing in YR-A, YR-B and YR-C units, and produce heavy oil and salt water from YR-D and YR-E as indicated by low resistivity log reading, and according to the Drill Steam Test (DST) with the description of cutting in final geological reports.

Lithofacies Classification of Carbonate Reservoirs Using Advanced Machine Learning: A Case Study from a Southern Iraqi Oil Field

2021 ◽  
Author(s):  
Mohammed A. Abbas ◽  
Watheq J. Al-Mudhafar
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Reservoir Characterization ◽  
Carbonate Reservoir ◽  
Oil Field ◽  
Machine Learning Techniques ◽  
Classification Algorithms ◽  
Classification Rate ◽  
Porosity And Permeability ◽  
Lithofacies Classification

Abstract Estimating rock facies from petrophysical logs in non-cored wells in complex carbonates represents a crucial task for improving reservoir characterization and field development. Thus, it most essential to identify the lithofacies that discriminate the reservoir intervals based on their flow and storage capacity. In this paper, an innovative procedure is adopted for lithofacies classification using data-driven machine learning in a well from the Mishrif carbonate reservoir in the giant Majnoon oil field, Southern Iraq. The Random Forest method was adopted for lithofacies classification using well logging data in a cored well to predict their distribution in other non-cored wells. Furthermore, three advanced statistical algorithms: Logistic Boosting Regression, Bagging Multivariate Adaptive Regression Spline, and Generalized Boosting Modeling were implemented and compared to the Random Forest approach to attain the most realistic lithofacies prediction. The dataset includes the measured discrete lithofacies distribution and the original log curves of caliper, gamma ray, neutron porosity, bulk density, sonic, deep and shallow resistivity, all available over the entire reservoir interval. Prior to applying the four classification algorithms, a random subsampling cross-validation was conducted on the dataset to produce training and testing subsets for modeling and prediction, respectively. After predicting the discrete lithofacies distribution, the Confusion Table and the Correct Classification Rate Index (CCI) were employed as further criteria to analyze and compare the effectiveness of the four classification algorithms. The results of this study revealed that Random Forest was more accurate in lithofacies classification than other techniques. It led to excellent matching between the observed and predicted discrete lithofacies through attaining 100% of CCI based on the training subset and 96.67 % of the CCI for the validating subset. Further validation of the resulting facies model was conducted by comparing each of the predicted discrete lithofacies with the available ranges of porosity and permeability obtained from the NMR log. We observed that rudist-dominated lithofacies correlates to rock with higher porosity and permeability. In contrast, the argillaceous lithofacies correlates to rocks with lower porosity and permeability. Additionally, these high-and low-ranges of permeability were later compared with the oil rate obtained from the PLT log data. It was identified that the high-and low-ranges of permeability correlate well to the high- and low-oil rate logs, respectively. In conclusion, the high quality estimation of lithofacies in non-cored intervals and wells is a crucial reservoir characterization task in order to obtain meaningful permeability-porosity relationships and capture realistic reservoir heterogeneity. The application of machine learning techniques drives down costs, provides for time-savings, and allows for uncertainty mitigation in lithofacies classification and prediction. The entire workflow was done through R, an open-source statistical computing language. It can easily be applied to other reservoirs to attain for them a similar improved overall reservoir characterization.

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