Tight Jurassic Carbonate Reservoir Characterization and Fluid Typing Identification by Integrating Magnetic Resonance, Elemental Spectroscopy and Micro-Resistivity Image Data in Umm Ross Field, West Kuwait, Case Study

2021 ◽  
Author(s):  
Said Beshry Mohamed ◽  
Sherif Ali ◽  
Mahmoud Fawzy Fahmy ◽  
Fawaz Al-Saqran
Keyword(s):  
Magnetic Resonance ◽  
Reservoir Characterization ◽  
Clay Content ◽  
Effective Porosity ◽  
Carbonate Reservoir ◽  
High Resistivity ◽  
Permeability Index ◽  
Rock Quality ◽  
Porosity And Permeability ◽  
Bulk Volume

Abstract The Middle Marrat reservoir of Jurassic age is a tight carbonate reservoir with vertical and horizontal heterogeneous properties. The variation in lithology, vertical and horizontal facies distribution lead to complicated reservoir characterization which lead to unexpected production behavior between wells in the same reservoir. Marrat reservoir characterization by conventional logging tools is a challenging task because of its low clay content and high-resistivity responses. The low clay content in Marrat reservoirs gives low gamma ray counts, which makes reservoir layer identification difficult. Additionally, high resistivity responses in the pay zones, coupled with the tight layering make production sweet spot identification challenging. To overcome these challenges, integration of data from advanced logging tools like Sidewall Magnetic Resonance (SMR), Geochemical Spectroscopy Tool (GST) and Electrical Borehole Image (EBI) supplied a definitive reservoir characterization and fluid typing of this Tight Jurassic Carbonate (Marrat formation). The Sidewall Magnetic resonance (SMR) tool multi wait time enabled T2 polarization to differentiate between moveable water and hydrocarbons. After acquisition, the standard deliverables were porosity, the effective porosity ratio, and the permeability index to evaluate the rock qualities. Porosity was divided into clay-bound water (CBW), bulk-volume irreducible (BVI) and bulk-volume moveable (BVM). Rock quality was interpreted and classified based on effective porosity and permeability index ratios. The ratio where a steeper gradient was interpreted as high flow zones, a gentle gradient as low flow zones, and a flat gradient was considered as tight baffle zones. SMR logging proved to be essential for the proper reservoir characterization and to support critical decisions on well completion design. Fundamental rock quality and permeability profile were supplied by SMR. Oil saturation was identified by applying 2D-NMR methods, T1/T2 vs. T2 and Diffusion vs. T2 maps in a challenging oil-based mud environment. The Electrical Borehole imaging (EBI) was used to identify fracture types and establish fracture density. Additionally, the impact of fractures to enhance porosity and permeability was possible. The Geochemical Spectroscopy Tool (GST) for the precise determination of formation chemistry, mineralogy, and lithology, as well as the identification of total organic carbon (TOC). The integration of the EBI, GST and SMR datasets provided sweet spots identification and perforation interval selection candidates, which the producer used to bring wells onto production.

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.

POROSITY AND PERMEABILITY OF RESERVOIRS AND CAPROCKS IN THE EROMANGA BASIN, SOUTH AUSTRALIA

1986 ◽  
Vol 26 (1) ◽  
pp. 202
Author(s):  
D.I. Gravestock ◽  
E.M. Alexander
Keyword(s):  
Grain Size ◽  
Gamma Ray ◽  
Size Variation ◽  
South Australia ◽  
Clay Content ◽  
Effective Porosity ◽  
Overburden Pressure ◽  
Porosity Reduction ◽  
Porosity And Permeability ◽  
Eromanga Basin

When effective porosity and permeability are measured at simulated overburden pressure, and grain size variation is taken into account, two distinct relationships are evident for Eromanga Basin reservoirs. Reservoirs in the Hutton Sandstone and Namur Sandstone Member behave such that significant porosity reduction can be sustained with retention of high permeability, whereas permeability of reservoirs in the Birkhead Formation and Murta Member is critically dependent on slight porosity variations. Logging tool responses are compared with core-derived data to show in particular the effects of grain size and clay content on the gamma ray, sonic, and density tools, where clay content is assessed from cation exchange capacity measurements. Sonic and density crossplots, constructed to provide comparison with a water-saturated 'reference' reservoir, are advantageous in comparing measured effective porosity from core plugs at overburden pressure with porosity calculated from logs. Gamma ray and sonic log responses of the Murta Member in the Murteree Horst area are clearly distinct from those of all other reservoirs, perhaps partly due to differences in mineralogy and shallower depth of burial compared with other formations.

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.

scholarly journals The Use of Nuclear Magnetic Resonance (NMR) Measurements and Conventional Logs to Predict Permeability for a Complex Carbonate Formation

2021 ◽  
Vol 11 (3) ◽  
pp. 82-98
Author(s):  
Raniah S. Alkhayyat ◽  
Fadhil S. Kadhim ◽  
Yousif khalaf Yousif
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Reservoir Characterization ◽  
Oil Field ◽  
Log Analysis ◽  
Petrophysical Properties ◽  
Carbonate Formation ◽  
Porosity And Permeability ◽  
Conventional Logs ◽  
Nuclear Magnetic

Permeability is one of the most important property for reservoir characterization, and its prediction has been one of the fundamental challenges specially for a complex formation such as carbonate, due to this complexity, log analysis cannot be accurate enough if it’s not supported by core data, which is critically important for formation evaluation. In this paper, permeability is estimated by making both core and log analysis for five exploration wells of Yammama formation, Nasiriyah oil field. The available well logging recorders were interpreted using Interactive Petrophysics software (IP) which used to determine lithology, and the petrophysical properties. Nuclear Magnetic Resonance (NMR) Measurements is used for laboratory tests, which provide an accurate, porosity and permeability measurements. The results show that the main lithology in the reservoir is limestone, in which average permeability of the potential reservoir units’ values tend to range from 0.064275 in zone YA to 20.74 in zone YB3, and averaged porosity values tend to range from 0.059 in zone YA to 0.155 in zoneYB3. Zone YB3 is found to be the best zone in the Yammama formation according to its good petrophysical properties. The correlation of core-log for permeability and porosity produce an acceptable R^2 equal to 0.618, 0.585 respectively

Tight Carbonate Reservoir Characterization and Perforation Optimization Using Magnetic Resonance in Horizontal Well

2019 ◽  
Author(s):  
Cahyo Nugroho ◽  
Mahmoud Fawzy Fahmy ◽  
Dipak Singha Ray ◽  
Mohamed Zekraoui ◽  
Riyad Qutainah ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Horizontal Well ◽  
Reservoir Characterization ◽  
Carbonate Reservoir ◽  
Tight Carbonate ◽  
Carbonate Reservoir Characterization

Characterization of Tight Carbonate Reservoir by Using Nuclear Magnetic Resonance Log Analysis in the Bahrain Field

2021 ◽  
Author(s):  
Rabab Al Saffar ◽  
Michael Dowen
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Carbonate Reservoir ◽  
Production Performance ◽  
High Porosity ◽  
Produce Water ◽  
Oil Saturation ◽  
Rock Quality ◽  
Core Study ◽  
Nuclear Magnetic

Abstract The Bahrain Field (the "Field"), discovered in 1932, is an asymmetric anticline trending in a North-South direction of the Kingdom of Bahrain. It is a geologically complex field with 16 multi-stack carbonate and sandstone reservoirs, most of them oil bearing. The fluids varying from shallow tarry oil in Aruma to dry gas in the Khuff and pre-Khuff reservoirs. The Field has more than 2000 wells of which 90% have good quality log data. The Ostracod and Magwa reservoirs are heterogeneous, layered tight reservoirs and have been on production since 1964. The Ostracod reservoir consists of very heterogeneous with limestone intervals intercalated between shale layers, with a total thickness of around 200 ft. The Magwa reservoir conformably underlies the Ostracod reservoir. The Ostracod averages 120 ft in thickness and is dominated by limestone with high porosity, low permeability, and variable water saturations. Core derived permeability measurements are usually less than 5 mD and porosities average 22%. Production performance of individual wells is extremely variable and in many cases appears to be at odds with log-calculated saturations. Wells having good oil saturation often produce water and wells with low oil saturation produce high volumes of oil. Several studies have been conducted in an attempt to understand and resolve this. The variability of oil saturation which has been mapped both laterally across the Field and vertically within wells, led to the question of what caused the variation in oil saturation. The variation is not a function of depth, which one might expect. Causes might include oil failure to migrate into certain reservoir compartments, a loss of the original charge to shallower reservoir or the oil charge been restricted by rock quality. This paper attempts to address the variability in saturations seen across the Field and link known productivity to the Petrophysical interpretations. Nuclear Magnetic Resonance (NMR) logs had been employed in a targeted area of the Field in order to investigate rock quality in an attempt to explain the oil saturation distribution. A small NMR core study was undertaken in order to calibrate the NMR log response. The NMR data had been initially processed with what was considered a representative cut-off for Middle East Carbaonte rocks. This core study resulted in a surprisingly low series of T2 cut-off. The NMR logs were reprocessed with the more representative T2 cut-off. The resulting bound and free fluid fractions seemed to explain the observed well production.

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