scholarly journals Stratigraphic variability of Mississippian Meramec chemofacies and petrophysical properties using Machine Learning and geostatistical modeling,STACK trend, Anadarko Basin, Oklahoma

2021 ◽  
pp. 1-59
Author(s):  
Laynie Hardisty ◽  
Matthew J. Pranter ◽  
Deepak Devegowda ◽  
Kurt J. Marfurt ◽  
Carl Sondergeld ◽  
...  
Keyword(s):  
Water Saturation ◽  
Development Planning ◽  
Effective Porosity ◽  
Well Logs ◽  
Blind Test ◽  
Cross Sectional ◽  
Anadarko Basin ◽  
Rock Types ◽  
Calcite Cement ◽  
Stacking Patterns

Mississippian Meramec deposits and reservoirs in the Sooner Trend in the Anadarko (Basin) in Canadian and Kingfisher counties (STACK) play of central Oklahoma are comprised of silty limestones, calcareous sandstones, argillaceous-calcareous siltstones, argillaceous siltstones, and mudstones. We have used core-derived X-ray fluorescence (XRF) data and established environmental proxies to evaluate the occurrence of specific elements (Al, K, Ti, Zr, Sr, Ca, and Si) and to illustrate their stratigraphic variability. For the Mississippian Meramec, six indicator elements or element ratios serve as proxies for clay (Al and K), detrital sediment (Ti and Zr), carbonate deposits (Sr and Ca), calcite cement (Sr/Ca), and biogenic and continentally derived quartz (Si/Ti and Si/Al). We used an unsupervised K-means classification to cluster elemental data from which we interpret three chemofacies: (1) calcareous sandstone, (2) argillaceous-calcareous siltstone, and (3) detrital mudstone. We used a random forest approach to relate core-derived chemofacies to well logs and classify chemofacies in noncored wells with an accuracy of up to 83% based on blind test results. We integrated core-derived XRF, conventional well logs, and chemofacies logs to produce a dip-oriented cross-sectional chemofacies model that trends from the northwest to the southeast across the southern STACK trend. Meramec chemofacies distributions reflect parasequence stacking patterns. The stratigraphic variability of chemofacies indicates an upward increase of argillaceous detrital mudstone from parasequences 1 to 3. Parasequence 3 is capped by a maximum flooding surface. From parasequences 4 to 5, an increase in argillaceous-calcareous siltstone and calcareous sandstone reflects the progradational stacking. Porosity is relatively low in calcareous sandstones primarily due to calcite cement. Water saturation is high in argillaceous-calcareous siltstone, moderate in calcareous sandstone, and low in detrital mudstone. Within the Meramec, biogenic quartz is associated with drilling issues, specifically frequent bit trips due to its hardness. Interpreted biogenic quartz from element profiles corresponds to the calcareous sandstone chemofacies, which can be estimated from triple-combo well logs and can be mapped. Effective porosity and water saturation models reflect the stratigraphic variability of chemofacies and rock types and can be predicted within the defined chemostratigraphic framework. Understanding the spatial variability of effective porosity and water saturation is important for reservoir development planning.

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 Hartha Formation divisions Based on Well Logs Analysis in Majnoon Oil Field, Southern Iraq

2020 ◽  
pp. 1362-1369
Author(s):  
Gheed Chaseb ◽  
Thamer A. Mahdi
Keyword(s):  
Water Saturation ◽  
Effective Porosity ◽  
Oil Field ◽  
Well Logs ◽  
Petrophysical Properties ◽  
Reservoir Properties ◽  
Thin Sections ◽  
Facies Associations ◽  
Log Interpretation ◽  
Well Logs Interpretation

This study aims to evaluate reservoir characteristics of Hartha Formation in Majnoon oil field based on well logs data for three wells (Mj-1, Mj-3 and Mj-11). Log interpretation was carried out by using a full set of logs to calculate main petrophysical properties such as effective porosity and water saturation, as well as to find the volume of shale. The evaluation of the formation included computer processes interpretation (CPI) using Interactive Petrophysics (IP) software.  Based on the results of CPI, Hartha Formation is divided into five reservoir units (A1, A2, A3, B1, B2), deposited in a ramp setting. Facies associations is added to well logs interpretation of Hartha Formation, and was inferred by a microfacies analysis of thin sections from core and cutting samples. The CPI shows that the A2 is the main oil- bearing unit, which is characterized by good reservoir properties, as indicated by high effective porosity, low water saturation, and low shale volume. Less important units include A1 and A3, because they have low petrophysical properties compared to the unit A2.

scholarly journals Mississippian Meramec Lithologies and Petrophysical Property Variability,STACK Trend, Anadarko Basin, Oklahoma

2021 ◽  
pp. 1-59
Author(s):  
Michael J. Miller ◽  
Matthew J. Pranter ◽  
Ishank Gupta ◽  
Deepak Devegowda ◽  
Kurt J. Marfurt ◽  
...  
Keyword(s):  
Pore Volume ◽  
Water Saturation ◽  
Rock Type ◽  
Clay Content ◽  
Anadarko Basin ◽  
Rock Types ◽  
Lower Porosity ◽  
Artificial Neural Network Ann ◽  
Type 3 ◽  
Maximum Flooding Surface

Mississippian Meramec reservoirs of the STACK (Sooner Trend in the Anadarko [Basin] in Canadian and Kingfisher counties) play are comprised of silty limestones, calcareous siltstones, argillaceous-calcareous siltstones, argillaceous siltstones and mudstones. We found that core-defined reservoir lithologies are related to petrophysics-based rock types derived from porosity-permeability relationships using a flow-zone indicator approach. We classified lithologies and rock types in non-cored wells using an Artificial Neural Network (ANN) with overall accuracies of 93% and 70%, respectively. We observed that mudstone-rich rock type 1 exhibits high clay and low calcite while calcareous-rich rock type 3 has high calcite and low clay content with rock type 2 falling in between rock types 1 and 3. Results of the ANN were applied to a suite of well logs in non-cored wells in which we generated lithology and rock-type logs. We identified that the Meramec consists of seven stratigraphic units characterized as strike-elongate, shoaling-upward parasequences; each parasequence is capped by a marine-flooding surface. The lower three parasequences (lower Meramec) form a retrogradational parasequence set that back-steps to the northwest and is capped by a maximum flooding surface. The upper Meramec is characterized by parasequences that form an aggradational to progradational stacking pattern followed again by a retrogradational trend. We predict that the parasequence stacking, associated lithology distribution, and diagenetic cements appear to control the spatial distribution of petrophysical properties (porosity, permeability, water saturation), pore volume, and hydrocarbon pore volume (HCPV). Calcareous-rich lithologies exhibit lower porosity, permeability, and HCPV and higher water saturation. Argillaceous-rich lithologies that occur near the maximum flooding surface are the most favorable reservoir intervals as they exhibit relatively higher porosity, permeability, HCPV, and lower water saturation. Productivity could not be directly correlated to rock types as operational and completion factors along with overpressure and oil phase play important roles on production.

scholarly journals Multiscale evaluation of a thin-bed reservoir

2021 ◽  
Vol 47 (1) ◽  
pp. 5-20
Author(s):  
Anita Lis-Śledziona
Keyword(s):  
High Resolution ◽  
Gamma Ray ◽  
Water Saturation ◽  
Forward Modeling ◽  
Effective Porosity ◽  
Thin Layers ◽  
Well Logs ◽  
Earth Model ◽  
High Resolution Data ◽  
Volume Porosity

A thin-bed laminated shaly-sand reservoir of the Miocene formation was evaluated using two methods: high resolution microresistivity data from the XRMI tool and conventional well logs. Based on high resolution data, the Earth model of the reservoir was defined in a way that allowed the analyzed interval to be subdivided into thin layers of sandstones, mudstones, and claystones. Theoretical logs of gamma ray, bulk density, horizontal and vertical resistivity were calculated based on the forward modeling method to describe the petrophysical properties of individual beds and calculate the clay volume, porosity, and water saturation. The relationships amongst the contents of minerals were established based on the XRD data from the neighboring wells; hence, the high-resolution lithological model was evaluated. Predicted curves and estimated volumes of minerals were used as an input in multimineral solver and based on the assumed petrophysical model the input data were recalculated, reconstructed and compared with the predicted curves. The volumes of minerals and input curves were adjusted during several runs to minimalize the error between predicted and recalculated variables. Another approach was based on electrofacies modeling using unsupervised self-organizing maps. As an input, conventional well logs were used. Then, the evaluated facies model was used during forward modeling of the effective porosity, horizontal resistivity and water saturation. The obtained results were compared and, finally, the effective thickness of the reservoir was established based on the results from the two methods.

WELL LOGS IN CARBONATE RESERVOIRS

Geophysics ◽  
1960 ◽  
Vol 25 (4) ◽  
pp. 779-853 ◽  
Author(s):  
L. G. Chombart
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Water Saturation ◽  
Decisive Role ◽  
Minimum Cost ◽  
Carbonate Reservoirs ◽  
Well Logs ◽  
Rock Types ◽  
Pore Structures

Modern well logs can play an important, often decisive role in the evaluation of carbonate reservoirs, and in well completions therein. To do so however, the logs must be selected and interpreted with due regard for the specific rock “types” and pore structures encountered by each well. Indeed, the basic condition stated applies to all evaluation and completion techniques now in use or conceivable. It becomes vitally important in carbonate reservoirs, however, because of their extraordinary heterogeneity. Characteristically, these reservoirs exhibit significant, often extreme, and always unpredictable variations in pore structure, pore size distribution and fluid content, within very short distances, in any direction. To cope with such a reservoir, an evaluation and logging program adhering to certain principles is most likely to yield valid results and insure better completions and greater ultimate recovery, at minimum cost. First, in every well, the cuttings or cores should be described precisely as to rock types and depths. Second, any techniques used should permit the largest possible number of determinations through the reservoir, so that any existing relationships between pore size distribution, porosity and water saturation may be established on a sound statistical basis. Among logging devices, “focusing” tools meet this requirement best. Third, starting very early in the development of the reservoir, the latter should be cored and logged in key wells, the cores subjected to capillary pressure and other petrophysical tests, and all potentially diagnostic logs run and analyzed in the light of all other data. Fourth, in non‐key wells, the logging program should include only those logs proved most reliable in the key wells for the pore structures encountered and the data desired (usually porosity, water saturation, net ft of pay).

scholarly journals Petrophysical Evaluation of Mauddud Formation in Selected Wells from Ratawi Oil Field, Southern Iraq

2021 ◽  
pp. 2956-2969
Author(s):  
Humam Q. Hameed ◽  
Afrah H. Saleh
Keyword(s):  
Water Saturation ◽  
High Water ◽  
Effective Porosity ◽  
Oil Field ◽  
Well Logs ◽  
Reservoir Properties ◽  
Oil Saturation ◽  
Shale Volume ◽  
Southern Iraq ◽  
Petrophysical Evaluation

    The objective of this paper is determining the petrophysical properties of the Mauddud Formation (Albian-Early Turonian) in Ratawi Oil Field depending on the well logs data by using interactive petrophysical software IP (V4.5). We evaluated parameters of available logs that control the reservoir properties of the formation, including shale volume, effective porosity, and water saturation. Mauddud Formation is divided into five units, which are distinguished by various reservoir characteristics. These units are A, B, C, D, and E. Through analyzing results of the computer processed interpretation (CPI) of available wells, we observed that the main reservoir units are B and D, being distinguished by elevated values of effective porosity (10%-32%) and oil saturation (95%-30%) with low shale content (6%-30%). Whereas, units A, C, and E were characterized by low or non-reservoir properties, due to high water saturation and low values of effective porosity caused by increased volume shale.

scholarly journals Hydrocarbon Reservoir Characterization of ‘Khume’ Field, Offshore Niger Delta, Nigeria

2021 ◽  
pp. 47-60
Author(s):  
K. A. Obakhume ◽  
O. M. Ekeng ◽  
C. Atuanya
Keyword(s):  
Niger Delta ◽  
Reservoir Characterization ◽  
Gamma Ray ◽  
Water Saturation ◽  
Seismic Interpretation ◽  
Effective Porosity ◽  
Well Logs ◽  
Integrative Approach ◽  
Neutron Density ◽  
Hydrocarbon Reservoir

The integrative approach of well log correlation and seismic interpretation was adopted in this study to adequately characterize and evaluate the hydrocarbon potentials of Khume field, offshore Niger Delta, Nigeria. 3-D seismic data and well logs data from ten (10) wells were utilized to delineate the geometry of the reservoirs in Khume field, and as well as to estimate the hydrocarbon reserves. Three hydrocarbon-bearing reservoirs of interest (D-04, D-06, and E-09A) were delineated using an array of gamma-ray logs, resistivity log, and neutron/density log suites. Stratigraphic interpretation of the lithologies in Khume field showed considerable uniform gross thickness across all three sand bodies. Results of petrophysical evaluations conducted on the three reservoirs correlated across the field showed that; shale volume ranged from 7-14%, total and effective porosity ranged from 19-26% and 17-23% respectively, NTG from 42 to 100%, water saturation from 40%-100% and permeability from 1265-2102 mD. Seismic interpretation established the presence of both synthetic and antithetic faults. A total of six synthetic and four antithetic faults were interpreted from the study area. Horizons interpretation was done both in the strike and dip directions. Time and depth structure maps revealed reservoir closures to be anticlinal and fault supported in the field. Hydrocarbon volumes were calculated using the deterministic (map-based) approach. Stock tank oil initially in place (STOIIP) for the proven oil column estimated for the D-04 reservoir was 11.13 MMSTB, 0.54 MMSTB for D-06, and 2.16 MMSTB for E-09A reservoir. For the possible oil reserves, a STOIIP value of 7.28 MMSTB was estimated for D-06 and 6.30 MMSTB for E-09A reservoir, while a hydrocarbon initially in place (HIIP) of 4.13 MMSTB of oil equivalents was derived for the undefined fluid (oil/gas) in D-06 reservoir. A proven gas reserve of 1.07 MMSCF was derived for the D-06 reservoir. This study demonstrated the effectiveness of 3-D seismic and well logs data in delineating reservoir structural architecture and in estimating hydrocarbon volumes

scholarly journals Stoneley Wave Predicted Permeability and Electrofacies Correlation in the Bangestan Reservoir, Mansouri Oilfield, SW Iran

2018 ◽  
Vol 57 (2) ◽  
Author(s):  
Bahman Soleimani ◽  
Mehrdad Moradi ◽  
Ali Ghabeishavi
Keyword(s):  
Reservoir Characterization ◽  
Gamma Ray ◽  
Water Saturation ◽  
Effective Porosity ◽  
Stoneley Wave ◽  
Stoneley Waves ◽  
Rock Types ◽  
The Matrix ◽  
Sw Iran ◽  
Neutron Porosity

 Reservoir characterization is one of the most important goals for the development of any oilfield. Determination of permeability and rock types are of prime importance to judge reservoir quality. In this research, Stoneley waves from dipole sonic tools were used in order to discover changes in permeability in the Bangestan reservoir, Mansouri oilfield. Index (tortuosity) could be estimated by Stoneley waves. After comparing the permeability resulting from Stoneley waves, cores and the Timur method, it was concluded that all the three permeabilities were very similar. The core porosity and effective porosity from the analysis of well logs were found to match as well. Electrofacies (EF) method, as a clustering method, was utilized to find rock types in order to define reservoir and non-reservoir zones. Simultaneous with EF clustering, gamma ray, neutron porosity, density, sonic, water saturation and porosity (PHIE) data from 78 wells were also considered and interpreted. Nine clusters were defined as a result of the analysis, being reduced to only four clusters after applying PC (capillary pressure) data. Among the four clusters, clusters 1 and 2 contained more vuggy pores than the others. Fracture abundance and solution seams were observed more frequently in EF-3 as compared to other EFs. Based on the matrix type, Archie porosity classification types I and III were recognized. The pore sizes in EFs-1 and 2 were mostly of the B type while in EF-3, it was A type. The EFs generated and determined by Stoneley waves and the well log data were also compared, showing a good correlation.

Relating Acoustic Anisotropy to Kerogen Content in Unconventional Formations - A Case Study in A Kerogen-Rich Unconventional Carbonate

2021 ◽  
Author(s):  
Yair Gordin ◽  
Thomas Bradley ◽  
Yoav O. Rosenberg ◽  
Anat Canning ◽  
Yossef H. Hatzor ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Water Saturation ◽  
Well Logs ◽  
Rock Properties ◽  
Thermal Maturity ◽  
Burial Diagenesis ◽  
S Wave ◽  
Velocity Anisotropy ◽  
Thermal Maturation ◽  
Wide Range

Abstract The mechanical and petrophysical behavior of organic-rich carbonates (ORC) is affected significantly by burial diagenesis and the thermal maturation of their organic matter. Therefore, establishing Rock Physics (RP) relations and appropriate models can be valuable in delineating the spatial distribution of key rock properties such as the total organic carbon (TOC), porosity, water saturation, and thermal maturity in the petroleum system. These key rock properties are of most importance to evaluate during hydrocarbon exploration and production operations when establishing a detailed subsurface model is critical. High-resolution reservoir models are typically based on the inversion of seismic data to calculate the seismic layer properties such as P- and S-wave impedances (or velocities), density, Poisson's ratio, Vp/Vs ratio, etc. If velocity anisotropy data are also available, then another layer of data can be used as input for the subsurface model leading to a better understanding of the geological section. The challenge is to establish reliable geostatistical relations between these seismic layer measurements and petrophysical/geomechanical properties using well logs and laboratory measurements. In this study, we developed RP models to predict the organic richness (TOC of 1-15 wt%), porosity (7-35 %), water saturation, and thermal maturity (Tmax of 420-435⁰C) of the organic-rich carbonate sections using well logs and laboratory core measurements derived from the Ness 5 well drilled in the Golan Basin (950-1350 m). The RP models are based primarily on the modified lower Hashin-Shtrikman bounds (MLHS) and Gassmann's fluid substitution equations. These organic-rich carbonate sections are unique in their relatively low burial diagenetic stage characterized by a wide range of porosity which decreases with depth, and thermal maturation which increases with depth (from immature up to the oil window). As confirmation of the method, the levels of organic content and maturity were confirmed using Rock-Eval pyrolysis data. Following the RP analysis, horizontal (HTI) and vertical (VTI) S-wave velocity anisotropy were analyzed using cross-dipole shear well logs (based on Stoneley waves response). It was found that anisotropy, in addition to the RP analysis, can assist in delineating the organic-rich sections, microfractures, and changes in gas saturation due to thermal maturation. Specifically, increasing thermal maturation enhances VTI and azimuthal HTI S-wave velocity anisotropies, in the ductile and brittle sections, respectively. The observed relationships are quite robust based on the high-quality laboratory and log data. However, our conclusions may be limited to the early stages of maturation and burial diagenesis, as at higher maturation and diagenesis the changes in physical properties can vary significantly.

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.

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