Geostatistics assisted by machine learning for reservoir property modeling: A case study in presalt carbonates of Buzios Field, Brazil

2021 ◽  
Vol 40 (12) ◽  
pp. 876-885
Author(s):  
Danilo Jotta Ariza Ferreira ◽  
Gabriella Martins Baptista de Oliveira ◽  
Thais Mallet Castro ◽  
Raquel Macedo Dias ◽  
Wagner Moreira Lupinacci
Keyword(s):  
Effective Porosity ◽  
Reservoir Properties ◽  
Predictive Capacity ◽  
Seismic Attribute ◽  
Blind Test ◽  
The North ◽  
Basal Section ◽  
Petrophysical Modeling ◽  
Embedded Model ◽  
Porosity And Permeability

An embedded model estimator (EMBER) petrophysical modeling algorithm has been applied to obtain effective porosity and permeability within the presalt carbonate reservoirs of the Barra Velha Formation in Buzios Field, Santos Basin. This advanced methodology was used due to the heterogeneity and complexity of the reservoirs, which makes modeling by conventional geostatistical methodologies difficult. For effective porosity modeling, we chose one facies model, one stratigraphic seismic attribute (acoustic impedance), and one structural seismic attribute (local flatness) as secondary variables. Permeability was modeled by using the best effective porosity simulation result as a secondary variable. Our results demonstrate that average effective porosity and permeability were 0.10 v/v and 440 md, respectively, indicating good reservoir quality throughout the studied area. A vertical trend of high effective porosities and permeabilities for the basal and uppermost reservoir sections was identified in our results, as well as a trend with lower values for these reservoir properties for the intermediate reservoir section. The lower section of the formation presented more continuity, and we infer it to be the best reservoir interval. We observed two horizontal trends for these reservoir properties at the formation top: one of higher values aligned to the north–south direction at the structural highs and another of lower reservoir properties related to isolated structural lows within structural highs. Correlation between modeled results and the blind test ANP-1 well upscaled properties was high, and upscaled well-log property distributions were preserved in the EMBER simulations, proving the predictive capacity of the algorithm. Finally, conditional distributions analysis indicated that the basal section of the Barra Velha Formation presents higher uncertainty for the estimation of effective porosity. Even though this interval is considered to have the best reservoir characteristics, decision making should be done with caution for this section.

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 NEW DATA ON PROMISING OIL AND GAS OBJECTS IN THE SANDSTONES OF THE YAMNA SUITE OF THE PALEOCENE IN THE NORTHWEST OF THE SKIBA ZONE OF THE UKRAINIAN CARPATHIANS

2021 ◽  
pp. 90-110
Author(s):  
V.Ye. Shlapinskiy ◽  
H.Ya. Havryshkiv ◽  
Yu.P. Haievska
Keyword(s):  
Oil And Gas ◽  
Gas Production ◽  
Oil Field ◽  
Distribution Area ◽  
Total Production ◽  
Reservoir Properties ◽  
Oil And Gas Production ◽  
The North ◽  
Natural Oil ◽  
Porosity And Permeability

More than 6 million tons of the oil have been extracted in the Skybа Zone of the Ukrainian Carpathians. In particular, 4.2 million tons of oil (85.7% of total production) were obtained from the Yamna sandstones of Paleocene, which are characterized by satisfactory physical properties. Most of the areas of fields that exploited them are located in the Boryslav oil and gas production area. Among them are such oil fields as Skhidnytsko-Urytske (more than 3.8 million tons of oil extracted), Violeta, Faustina, MEP, Miriam and Ropne. Outside this area, oil was extracted in Strilbychi and Staraya Sol. At most of these fields, oil horizons are at a depth of only 100-800 m. The gas and condensate are extracted at the field of Tanyavа in the wing of the Vytvytska Luska of the Berehova Skyba, which has been torn off by the thrust. In addition, a very large number of natural oil and gas manifestations - direct signs of oil and gas potential - have been recorded in the Skyba Zone. All this indicates the potential prospects of structures within the Skyba Zone, including shallow ones. The distribution area of Yamna sandstones is much larger than the area of these deposits. The distribution area of sandstones of Yamna is much larger than the area of these deposits. It occupies about half of the area of Skyba Zone. Part of it can be considered promising, removing areas where of Yamna sandstones are present on the day surface, although, even in such conditions, they are in some cases industrially oil-bearing (Strelbychi oil field). Sandstones of Yamna are characterized by satisfactory reservoir properties., The calculated porosity and permeability reach the maximum values at known deposits: 0.182 and 130 ∙ 10–3 microns2 respectively, and the estimated thickness of 13.5 m. In the Folded Carpathians and, especially, within the north-eastern fragments (Beregova, Oriv, Skoliv) in different years performed a large amount of field seismic surveys. On the basis of the obtained materials, for the first time in the Carpathian region structural constructions were made on the reflecting horizons in the Paleocene (Yamna Formation) and in the Stryi Formation of the Upper Cretaceous. This article evaluates the prospects of these research objects. The Khodkiv and Osichnyanska structures of Berehova Skyba are recommended for conducting search works.

scholarly journals Seismic attribute and petrophysics-assisted interpretation of the Nanushuk and Torok Formations on the North Slope, Alaska

2020 ◽  
Vol 8 (2) ◽  
pp. SJ17-SJ34 ◽  
Author(s):  
Shuvajit Bhattacharya ◽  
Sumit Verma
Keyword(s):  
Oil And Gas ◽  
Gamma Ray ◽  
Regional Scale ◽  
Rock Physics ◽  
Seismic Attributes ◽  
North Slope ◽  
Reservoir Properties ◽  
Seismic Attribute ◽  
Seismic Reflection Data ◽  
The North

Exploration of the Brookian sandstone reservoirs in the Nanushuk and Torok Formations on the North Slope of Alaska is a hot topic and presents opportunities to the oil and gas community because of their shallow depth, vast extent, and scope of development. The consecutive hydrocarbon discoveries announced by Repsol-Armstrong, Caelus Energy, and ConocoPhillips in 2015, 2016, and 2017 have indicated the presence of the vast recoverable resources on the North Slope in the Nanushuk and Torok reservoirs. We have investigated the detailed geophysical and petrophysical characteristics of these reservoirs. Our goal is to detect dominant geologic features in these formations using a combination of seismic attributes at the regional scale and analyze critical petrophysical and rock physics properties to evaluate formation heterogeneities and identify the reservoir targets by integrating well log and core data at the well scale. The Nanushuk Formation is expressed as topset reflections, whereas the Torok and gamma-ray zone formations are expressed as foresets and bottomsets on the seismic reflection data. Using seismic attributes, we mapped the extent of different geomorphological features, including shelf edges, channels, slides, and basin-floor fans, all with significant amplitude anomalies. The shelf edges continue for tens to hundreds of miles along the north/northwest and east–west directions, depending on the areas. The internal characters of these formations delineated by conventional well logs and advanced petrophysical analysis reveal their vertical heterogeneities and complexities, in terms of reservoir properties. We conclude that the reservoirs are vertically and laterally heterogeneous. These are thin-bedded low-resistivity reservoirs. Only a few zones in the parasequences are oil-saturated. We find that a combination of low [Formula: see text] ratio and acoustic impedance can be a useful proxy to detect the hydrocarbon-bearing sand intervals in these formations.

Integrated Approach to Geostatistics For Optimal Reservoir Properties Distribution – Case Study of X-Reservoir in Niger Delta Basin

2021 ◽  
Author(s):  
Osita Robinson Madu ◽  
Jerry Orrelo Athoja ◽  
Amarachi Queen Kalu ◽  
Obi Mike Onyekonwu
Keyword(s):  
Integrated Approach ◽  
Geostatistical Analysis ◽  
Optimal Distribution ◽  
Reservoir Properties ◽  
Seismic Attribute ◽  
Reservoir Heterogeneity ◽  
Spatial Continuity ◽  
Porosity And Permeability ◽  
Well Data ◽  
Facies Types

Abstract In-depth knowledge of geostatistical analysis, environment of deposition and reservoir facies types is important for optimal distribution of reservoir properties across the reservoir grid. Geostatistics is a veritable tool that is quantitatively used to model spatial continuity, anisotropy direction and capture reservoir heterogeneity for optimal distribution of reservoir properties. When spatial continuity and heterogeneity level of the reservoir are adequately understood and modeled, representative property distribution becomes possible. In the face of limited well data, modeling major and minor directions of horizontal variogram is highly impaired and it becomes difficult to adequately distribute properties within the reservoir grid with enough control. This study is focused on the integration of seismic data, core data, well logs and geological knowledge to carry out geostatistical analysis to optimally distribute facies, porosity and permeability properties within the grid. The degree of reservoir heterogeneity was determined quantitatively using semivariogram and Lorenz plots of core porosity and permeability data. Variogram map generated from seismic attribute was used in combination with the sparse well data points to determine the horizontal variogram. The available well data was adequate enough to model the vertical variogram. The environment of deposition was interpreted as lower to upper shoreface with channel deposits and some shallow marine influence. The properties were normal-scored and modeled with the determined variogram parameters while biasing them with facies. Results of the semivariogram and Lorenz plots showed that the reservoir is fairly heterogenous in terms of spatial continuity. Major direction of the geological continuity is in the Northeast-Southwest direction while the minor direction is orthogonal to it. Final result of the modeled properties was in consonance with the facies types described from the environment of deposition.

Seismic attribute mapping of a fluvial reservoir in Rhourde Chegga field (Hassi Messaoud, Algeria)

2020 ◽  
Author(s):  
Nasrine Medjdouba ◽  
zahia benaissa ◽  
amar boudella
Keyword(s):  
Seismic Data ◽  
Lower Triassic ◽  
Reservoir Properties ◽  
Seismic Attribute ◽  
The North ◽  
Fluvial Reservoir ◽  
Attribute Analysis ◽  
The Relationship ◽  
Sand Bodies ◽  
Rms Amplitude

<p>Rhourde Chegga field is located in the north of Hassi Messaoud giant field, Algeria. The main hydrocarbon-bearing reservoir in Rhourde Chegga field is the lower Triassic Argilo-Gréseux reservoir. The Triassic sand is deposited as fluvial channels and overbank sands with a thickness ranging from 15 to 20 m, lying unconformably on the Paleozoic formations. Lateral and vertical distribution of the sand bodies makes their mapping very difficult and, sometimes, even impossible with conventional seismic interpretation. </p><p>To better define drilling targets within the Triassic sand in the Rhourde Chegga field, 3D stratigraphic seismic attribute analysis was performed along the reservoir level, using PSTM and mid angle stack seismic data. By combining various attributes (RMS amplitude, half energy, variance, etc.), the channelized feature has been clearly imaged and delineated on the horizon slices and the volume extraction. The relationship between the combined seismic attributes and reservoir properties at well locations showed a good correlation.</p><p>Based on this study, about ten produced wells have been successfully drilled, confirming the efficiency of seismic attribute analysis to predicted channel body geometry.</p><p>Keywords: Channel, Attributes, Amplitude, Fluvial reservoir.</p><p> </p>

scholarly journals Spatial variation in porosity and permeability of the Rupel Clay Member in the Netherlands

2016 ◽  
Vol 95 (3) ◽  
pp. 253-268 ◽  
Author(s):  
Hanneke Verweij ◽  
Geert-Jan Vis ◽  
Elke Imberechts
Keyword(s):  
The Netherlands ◽  
Spatial Variation ◽  
Clay Content ◽  
Burial Depth ◽  
Southeastern Part ◽  
Boom Clay ◽  
The North ◽  
Sealing Capacity ◽  
Porosity And Permeability ◽  
Permeability Data

AbstractThe spatial distribution of porosity and permeability of the Rupel Clay Member is of key importance to evaluate the spatial variation of its sealing capacity and groundwater flow condition. There are only a limited number of measured porosity and permeability data of the Rupel Clay Member in the onshore Netherlands and these data are restricted to shallow depths in the order of tens of metres below surface. Grain sizes measured by laser diffraction and SediGraph® in samples of the Rupel Clay Member taken from boreholes spread across the country were used to generate new porosity and permeability data for the Rupel Clay Member located at greater burial depth. Effective stress and clay content are important parameters in the applied grain-size based calculations of porosity and permeability.The calculation method was first tested on measured data of the Belgian Boom Clay. The test results showed good agreement between calculated permeability and measured hydraulic conductivity for depths exceeding 200m.The spatial variation in lithology, heterogeneity and also burial depth of the Rupel Clay Member in the Netherlands are apparent in the variation of the calculated permeability. The samples from the north of the country consist almost entirely of muds and as a consequence show little lithology-related variation in permeability. The vertical variation in permeability in the more heterogeneous Rupel Clay Member in the southern and east-southeastern part of the country can reach several orders of magnitude due to increased permeability of the coarser-grained layers.

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.

Machine Learning for Multiple Petrophysical Properties Regression Based on Core Images and Well Logs in a Heterogenous Reservoir

2021 ◽  
Author(s):  
Tao Lin ◽  
Mokhles Mezghani ◽  
Chicheng Xu ◽  
Weichang Li
Keyword(s):  
Machine Learning ◽  
Model Building ◽  
Image Features ◽  
Well Logs ◽  
Model Complexity ◽  
Rock Properties ◽  
Core Analysis ◽  
Petrophysical Properties ◽  
Blind Test ◽  
Porosity And Permeability

Abstract Reservoir characterization requires accurate prediction of multiple petrophysical properties such as bulk density (or acoustic impedance), porosity, and permeability. However, it remains a big challenge in heterogeneous reservoirs due to significant diagenetic impacts including dissolution, dolomitization, cementation, and fracturing. Most well logs lack the resolution to obtain rock properties in detail in a heterogenous formation. Therefore, it is pertinent to integrate core images into the prediction workflow. This study presents a new approach to solve the problem of obtaining the high-resolution multiple petrophysical properties, by combining machine learning (ML) algorithms and computer vision (CV) techniques. The methodology can be used to automate the process of core data analysis with a minimum number of plugs, thus reducing human effort and cost and improving accuracy. The workflow consists of conditioning and extracting features from core images, correlating well logs and core analysis with those features to build ML models, and applying the models on new cores for petrophysical properties predictions. The core images are preprocessed and analyzed using color models and texture recognition, to extract image characteristics and core textures. The image features are then aggregated into a profile in depth, resampled and aligned with well logs and core analysis. The ML regression models, including classification and regression trees (CART) and deep neural network (DNN), are trained and validated from the filtered training samples of relevant features and target petrophysical properties. The models are then tested on a blind test dataset to evaluate the prediction performance, to predict target petrophysical properties of grain density, porosity and permeability. The profile of histograms of each target property are computed to analyze the data distribution. The feature vectors are extracted from CV analysis of core images and gamma ray logs. The importance of each feature is generated by CART model to individual target, which may be used to reduce model complexity of future model building. The model performances are evaluated and compared on each target. We achieved reasonably good correlation and accuracy on the models, for example, porosity R2=49.7% and RMSE=2.4 p.u., and logarithmic permeability R2=57.8% and RMSE=0.53. The field case demonstrates that inclusion of core image attributes can improve petrophysical regression in heterogenous reservoirs. It can be extended to a multi-well setting to generate vertical distribution of petrophysical properties which can be integrated into reservoir modeling and characterization. Machine leaning algorithms can help automate the workflow and be flexible to be adjusted to take various inputs for prediction.

Depositional facies, diagenetic clay minerals and reservoir quality of Rotliegend sediments in the Southern Permian Basin (North Sea): a review

Clay Minerals ◽  
1982 ◽  
Vol 17 (1) ◽  
pp. 55-67 ◽  
Author(s):  
U. Seemann
Keyword(s):  
Clay Minerals ◽  
North Sea ◽  
Effective Porosity ◽  
Hydrocarbon Exploration ◽  
Reservoir Quality ◽  
Burial Diagenesis ◽  
Permian Basin ◽  
Northern Margin ◽  
The North

AbstractThe Southern Permian Basin of the North Sea represents an elongate E-W oriented depo-centre along the northern margin of the Variscan Mountains. During Rotliegend times, three roughly parallel facies belts of a Permian desert developed, these following the outline of the Variscan Mountains. These belts were, from south to north, the wadi facies, the dune and interdune facies, and the sabkha and desert lake facies. The bulk of the gas reservoirs of the Rotliegend occur in the aeolian dune sands. Their recognition, and the study of their geometry, is therefore important in hydrocarbon exploration. Equally important is the understanding of diagenesis, particularly of the diageneticaily-formed clay minerals, because they have an important influence on the reservoir quality of these sands. Clay minerals were introduced to the aeolian sands during or shortly after their deposition in the form of air-borne dust, which later formed thin clay films around the grains. During burial diagenesis, these clay films may have acted as crystallization nuclei for new clay minerals or for the transformation of existing ones. Depending on their crystallographic habit, the clay minerals can seriously affect the effective porosity and permeability of the sands.

scholarly journals Geostatistical Model for the Arab-D Reservoir, North ’Ain Dar Pilot, Ghawar Field, Saudi Arabia: An Improved Reservoir Simulation Model

GeoArabia ◽  
1996 ◽  
Vol 1 (2) ◽  
pp. 267-284
Author(s):  
John L. Douglas ◽  
Keyword(s):  
Saudi Arabia ◽  
Fluid Flow ◽  
Reservoir Rock ◽  
Well Test ◽  
Geostatistical Model ◽  
Deterministic Models ◽  
Modeling Approach ◽  
The North ◽  
Porosity And Permeability ◽  
Ghawar Field

ABSTRACT The North ‘Ain Dar 3-D geocellular model consists of geostatistical models for electrofacies, porosity and permeability for a portion of the Jurassic Arab-D reservoir of Ghawar field, Saudi Arabia. The reservoir consists of a series of shallow water carbonate shelf sediments and is subdivided into 10 time-stratigraphic slices on the basis of core descriptions and gamma/porosity log correlations. The North ‘Ain Dar model includes an electrofacies model and electrofacies-dependent porosity and permeability models. Sequential Indicator Simulations were used to create the electrofacies and porosity models. Cloud Transform Simulations were used to generate permeability models. Advantages of the geostatistical modeling approach used here include: (1) porosity and permeability models are constrained by the electrofacies model, i.e. by the distribution of reservoir rock types; (2) patterns of spatial correlation and variability present in well log and core data are built into the models; (3) data extremes are preserved and are incorporated into the model. These are critical when it comes to determining fluid flow patterns in the reservoir. Comparison of model Kh with production data Kh indicates that the stratigraphic boundaries used in the model generally coincide with shifts in fluid flow as indicated by flowmeter data, and therefore represent reasonable flow unit boundaries. Further, model permeability and production estimated permeability are correlated on a Kh basis, in terms of vertical patterns of distribution and cumulative Kh values at well locations. This agreement between model and well test Kh improves on previous, deterministic models of the Arab-D reservoir and indicates that the modeling approach used in North ‘Ain Dar should be applicable to other portions of the Ghawar reservoir.

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