Petrophysical Facies Research Articles

Classification of rock types of porous limestone reservoirs: case study of the A oilfield

Rock types with similar lithological components and pore structures form the basic units of porous limestone reservoirs; this influences the reservoir evaluation efficiency and water injection development. As the main oil and gas pay zone in central Iraq, the Cretaceous Khasib Formation reservoirs are influenced by deposition, dissolution, and cementation. There is strong vertical heterogeneity in the most important zone of the Kh2 layer, with diverse rock types and complex pore structures. Based on core observation and casting thin-section identification, the Kh2 layer in the study area was divided into eight lithofacies types as argillaceous bioclastic wackestone, planktic foraminiferium wackestone, lamellar bioclastic wackestone, intraclastic–bioclastic packstone, patchy green algae packstone, green algae and pelletoid packstone, benthic foraminiferium–bioclastic packstone, and intraclastic grainstone. Along with the reservoir void space types of the lithofacies, capillary pressure curves are used to quantitatively analyze the throat and pore features of the different lithofacies. From the porosity–permeability cross-plot characteristics and distribution of pore types, 14 petrophysical facies are obtained. Finally, based on the differences between the lithofacies and petrophysical facies, the Kh2 member is divided into 13 rock types with different geological origins and petrophysical characteristics. Among these, the rock type RT1-8-14 has the best and rock type RT1-1-1 has the worst physical properties among the reservoir rock types. This study provides an optimization method for carbonate reservoir evaluation and is expected to be beneficial for efficient development of similar carbonate reservoirs.

Log interpretation of carbonate rocks based on petrophysical facies constraints

The complex pore structure of carbonate reservoirs hinders the correlation between porosity and permeability. In view of the sedimentation, diagenesis, testing, and production characteristics of carbonate reservoirs in the study area, combined with the current trends and advances in well log interpretation techniques for carbonate reservoirs, a log interpretation technology route of “geological information constraint + deep learning” was developed. The principal component analysis (PCA) was employed to establish lithology identification criteria with an accuracy of 91%. The Bayesian stepwise discriminant method was used to construct a sedimentary microfacies identification method with an accuracy of 90.5%. Based on production data, the main lithologies and sedimentary microfacies of effective reservoirs were determined, and 10 petrophysical facies with effective reservoir characteristics were identified. Constrained by petrophysical facies, the mean interpretation error of porosity compared to core analysis results is 2.7%, and the ratio of interpreted permeability to core analysis is within one order of magnitude, averaging 3.6. The research results demonstrate that deep learning algorithms can uncover the correlation in carbonate reservoir well logging data. Integrating geological and production data and selecting appropriate machine learning algorithms can significantly improve the accuracy of well log interpretation for carbonate reservoirs.

A New Method for Evaluating Fracability Based on Cluster Analysis and Well Logging Petrophysical Facies Division

The fracability of rock is a crucial parameter in determining the efficiency of fracturing in tight reservoirs. However, current methods for evaluating fracability have significant limitations. This paper proposes a new method for evaluating reservoir fracability based on cluster analysis of logging petrophysical facies. This method considers rock mechanics characteristics, petrophysical response characteristics, and reservoir physical parameters, among other factors. The Well X of the block was used as the research subject. Using the brittleness index and permeability evaluation index, the reservoir fracability of the target block is classified into five categories: high brittleness and low permeability rock, high brittleness and super low permeability rock, low brittleness and low permeability rock, low brittleness and super low permeability rock, and clay/shale. Geolog software is used to generate fracability prediction profiles for representative wells based on petrophysical facies. To further validate this evaluation method, the fracture construction data and microseismic monitoring interpretation results of Well Y in a tight block of an oil field were synthesized and analyzed to predict its fracability. The overall prediction accuracy reached 81.8%, indicating that the new fracability evaluation method has good application effect.

Logging evaluation of petrophysical facies based reservoir quality prediction method for the volcanic rocks in the deep depth of Nanpu Sag, China

The volcanic rocks in the deep depth of Nanpu sag is the main formation of oil and gas enrichment. It has huge resource potential and prospects for exploration and development. Accurate characterization of lithology and pore space is the basis of reservoir quality prediction. Through petrography studies of cores and thin sections, explosive and sedimentary facies represent the dominant oil and gas-bearing reservoirs, and intergranular pores and fractures are major pore and seepage space. In order to predict favorable reservoirs and improve the efficiency of fracturing, according to composite well loggings and electrical images, logging response features of different lithofacies are characterized and five types of pore structure facies are classified. Further, five types of petrophysical facies are proposed. Among them, Type I and Type II are the most favorable petrophysical facies in the study area. The petrophysical facies typing approach show consistency with oil test data, which verifies that this approach can well predict reservoir quality and provide technical support for the exploration and development of the oil and gas-bearing volcanic rocks.

Evaluation of pore-throat structures of carbonate reservoirs based on petrophysical facies division

After complex geological transformation, such as sedimentation, diagenesis, and tectonic transformation, carbonate reservoirs have developed multiple reservoir spaces with significant heterogeneity and various pore-throat structures, including pores, vugs, and fractures. Due to these characteristics, many problems have been caused during the development of carbonate reservoirs, such as large differences between the initial production capacities of single wells and serious water channeling after injection, which have brought many challenges to the efficient development of carbonate reservoirs. Therefore, based on a reservoir evaluation method of petrophysical facies, this study classified and evaluated the pore-throat structures of Carboniferous carbonate reservoirs in the North Truwa oilfield of the Pre-Caspian Basin. Based on the data of the core, thin section, scanning electron microscope, high-pressure mercury injection, sedimentary facies, diagenetic facies, and the development of fractures and karst vugs, six reservoir petrophysical facies in the study area were classified and named, including the facies of favorable sedimentation-constructive diagenesis-fracture and vug, the facies of favorable sedimentation-constructive diagenesis-non-fracture or vug, the facies of favorable sedimentation-destructive diagenesis-fracture and vug, the facies of favorable sedimentation-destructive diagenesis-non-fracture or vug, the facies of unfavorable sedimentation-constructive diagenesis-non-fracture or vug, and the facies of unfavorable sedimentation-destructive diagenesis-non-fracture or vug. The recognition and division of diagenetic facies were based on the K-nearest neighbor. Four quantitative parameters were also selected to characterize the pore-throat structures, including displacement pressure (Pd), mercury removal efficiency (Em), median throat radius (R50), and sorting coefficient (Cs). Based on the principal component analysis, the classification and evaluation standards of pore-throat structures in different petrophysical facies were established. Combined with the flow profile measured by production logging tools, the relationship between different petrophysical facies and productions was also revealed. As a result, this study can provide suggestions for the adjustment strategy of water-driven production, which lays an important foundation for the fine development of carbonate reservoirs.

“Double sweet spot” identification method via cluster analysis of petrophysical properties from well logg data: A case study of a tight sandstone reservoir

In recent years, interest in unconventional resource plays has grown substantially due to horizontal drilling and hydraulic fracturing techniques. However, the high cost of drilling, choosing the right locations for perforation intervals during hydraulic fracturing is a crucial issue. Therefore, identifying so called “sweet spot” with high potential for hydrocarbon production plays an important role in oil and gas industry. In this study a new “double sweet spot” identification method using cluster analysis of well-logged petrophysical properties of facies is presented. Firstly, an engineering sweet spot identification method based on geophysical responses parameters (brittleness index and permeability) is introduced to classify the target zone. Triaxial compression tests are performed to obtain the static Young's modulus and Poisson's ratio of the rock samples. To calculate dynamic Young's modulus and Poisson's ratio, a novel model for determining interval transit time of S-wave is proposed based on the multiple linear regression theory according to well logging data. By performing the cluster analysis, an intersection diagram of permeability and calculated normalized Rickman's brittleness is built and then engineering sweet spots in target zone are characterized based on well-logged petrophysical properties of facies. By plotting the intersection diagram of porosity and oil saturation based on the cluster analysis theory, geological sweet spots in target zone were characterized and four types of rock petrophysical facies are divided: Sandstone A (So＞48%, 8＜φ＜19%); Sandstone B (31＜So＜48%, 6＜φ＜18%); Sandstone C (18＜So＜31%, 8＜φ＜20%) and Sandstone D (0＜So＜18%, 8＜φ＜17%). Secondly, target formation classification is carried out using a proposed geological sweet spot identification method based on reservoir physical parameters (oil saturation and porosity). The relationship between core electrical resistivity and water saturation is determined by traditional Archie's formula and then an oil saturation prediction model in target reservoir is established. Based on the experimental porosity and permeability measurements of 47 core samples and well logging data, a porosity interpretation model is proposed. By plotting the intersection diagram of porosity and oil saturation based on the cluster analysis theory, geological sweet spots in target formation are characterized and four types of rock petrophysical facies are given: Sandstone A (K＞0.1mD, BI＞38); Sandstone B (K＞0.1mD, 33＜BI＜38); Sandstone C (K＞0.1mD, BI＜33) and Sandstone D (0.01＜K＜0.1mD, 10＜BI＜60). The integrated “double sweet spot” identification results of Well 1 in Daqing tight sandstone oil reservoir are given. Rock AA is the most favorable type of petrophysical rock facies and rock DD is the worst favorable type of petrophysical rock facies. Finally, an integrated “double sweet spot” identification method proposed in this study was applied to screen proper interval candidates during refracturing operation in a tight sandstone oil reservoir in China. After refracturing operation a gradually increase of average oil production in well group was found from 1.10 t/d to 5.63 t/d. This novel “double sweet spot” identification method has applicability in other tight oil reservoirs around the world.

Spatial variability of petrofacies using supervised machine learning and geostatistical modeling: Sycamore Formation, Sho-Vel-Tum Field, Oklahoma, USA

The Sycamore Formation at the Sho-Vel-Tum Field primarily consists of clay-rich mudstones (Mdst) and quartz-rich siltstones. The clay-rich Mdst are mainly composed of clays, quartz grains, some allochems, and detrital organic matter. The siltstones are structureless and are divided into two petrofacies: high porosity and permeability massive calcareous siltstones and low porosity and permeability massive calcite-cemented siltstones. Core and well-log data provide mineralogical, lithologic, and porosity information that is useful to define petrophysical facies (petrofacies) and to create facies logs within the Sycamore Formation. We used the data to establish the Sycamore Formation stratigraphic architecture and to map its spatial variability and reservoir properties. To classify the Sycamore Formation petrofacies in noncored wells, we developed a machine learning-based workflow that compares more than 1800 classification models and selects the best combination of well logs, algorithms, and hyperparameters to predict defined petrofacies. The process includes combinations of well logs that were optimized in four classification algorithms: artificial neural network, K-nearest neighbor, support vector machine, and random forest. To adjust each classifier, we used a grid search and a fivefold cross validation to find the best combination of three hyperparameters to improve results of each algorithm. This workflow allows for the efficient extraction of information from cores at a low cost. After we generated the petrofacies logs in noncored wells, we combined them with multiple constraints to create a 3D petrofacies model for the Sycamore Formation at the Sho-Vel-Tum Field and analyze the stratigraphic and diagenetic controls on petrofacies and its impact in reservoir quality.

Evaluation of low permeability conglomerate reservoirs based on petrophysical facies: A case study from the Triassic Baikouquan Formation, northern Mahu Sag, Junggar Basin, China

The determination of high-quality intervals in oil reservoirs has always been challenging work while their petrophysical attributes are relatively low. To date, extensive reservoir evaluation studies have been undertaken to locate favorable zones for oil exploration and exploitation. However, without the geological constraint principles, the selected evaluation parameters depend heavily on experience or pure mathematical methods. Hence, we propose a reservoir evaluation method based on petrophysical facies to undertake the reservoir quality assessment for the conglomerate reservoirs of the Triassic Baikouquan Formation in the northern Mahu sag, Junggar Basin, western China. The lithofacies and diagenetic facies were first identified based on laboratory data. Subsequently, petrophysical facies types were recognized by the combination of lithofacies and diagenetic facies. Afterward, capillary pressure curves of different petrophysical facies were clustered to clarify the reservoir quality variation in distinct geological origins. Consequently, the evaluation standard was established and the reservoir quality distribution was analyzed and drawn out. These results indicate that there exist 4 rock types in the Baikouquan Formation (from RT1 to RT4). The distributional status of the optimal reservoir (RT1) and the worst reservoir (RT4) are primarily determined by grain size, while rock type 2 (RT2) and rock type 3 (RT3) are predominantly controlled by dissolution and compaction. Moreover, an evident positive association exists between the rock type and the single well production capacity, which reveals that the utilization of petrophysical facies to assess reservoir quality is reliable. It is concluded that reservoir evaluation based on petrophysical facies harbors the potential to be a pivotal tool for reservoir property assessment and prediction of favorable porosity and permeability zones.

Characterization and Evaluation of Carbonate Reservoir Pore Structure Based on Machine Learning

The carboniferous carbonate reservoirs in the North Truva Oilfield have undergone complex sedimentation, diagenesis and tectonic transformation. Various reservoir spaces of pores, caves and fractures, with strong reservoir heterogeneity and diverse pore structures, have been developed. As a result, a quantitative description of the pore structure is difficult, and the accuracy of logging identification and prediction is low. These pose a lot of challenges to reservoir classification and evaluation as well as efficient development of the reservoirs. This study is based on the analysis of core, thin section, scanning electron microscope, high-pressure mercury injection and other data. Six types of petrophysical facies, PG1, PG2, PG3, PG4, PG5, and PG6, were divided according to the displacement pressure, mercury removal efficiency, and median pore-throat radius isobaric mercury parameters, combined with the shape of the capillary pressure curve. The petrophysical facies of the wells with mercury injection data were divided accordingly, and then the machine learning method was applied. The petrophysical facies division results of two mercury injection wells were used as training samples. The artificial neural network (ANN) method was applied to establish a training model of petrophysical facies recognition. Subsequently, the prediction for the petrophysical facies of each well in the oilfield was carried out, and the petrophysical facies division results of other mercury injection wells were applied to verify the prediction. The results show that the overall coincidence rate for identifying petrophysical facies is as high as 89.3%, which can be used for high-precision identification and prediction of petrophysical facies in non-coring wells.

Analysis of Factors of Productivity of Tight Conglomerate Reservoirs Based on Random Forest Algorithm

The tight conglomerate reservoir of Baikouquan formation in the MA 131 well block in the Junggar basin abounds with petroleum reserves, yet the vertical wells in this reservoir have achieved a limited development effect. The tight conglomerate reservoirs have become an important target for exploration and exploitation. The high-efficiency development scheme of a small well spacing three-dimensional (3D) staggered well pattern has been determined by a series of field tests on well pattern and well spacing development. Multistage fracturing with a horizontal well has been demonstrated as the primary development technology. The horizontal wells in the MA 131 small well spacing demonstration area have achieved significantly different development effects, and the major controlling factors for high and stable production of a single well remain unclear. In this study, we proposed an evaluation model of major productivity controlling factors of the tight conglomerate reservoir to provide a reference for oil recovery based on a random forest (RF) machine-learning algorithm. The productivity factors were investigated from two aspects: petrophysical facies that are capable of indicating the genetic mechanism of geological dessert and engineering dessert parameters forming complex fracture networks. Resultantly, the reservoir in the MA 131 well block can be classified into 12 petrophysical facies according to the sedimentary characteristics and diagenesis analysis. The mercury injection curves of a variety of petrophysical facies can be classified into four reservoir quality types. The RF model was trained on 80% of the data to predict the oil well class using the selected features as primary inputs while the remaining 20% of the data were set to test the model performance. The results indicated that the RF model produced excellent results with only 12 misclassifications across the entire data set of 627 samples that represent <2% error. The important evaluation score of the random forest algorithm model showed that the reservoir type, oil saturation, horizontal stress difference, and gravel content are the most important four indicators, with each value exceeding 15%. Brittleness and maximum horizontal stress are considered the least important indexes, with values of less than 5%. Reservoir quality and oil saturation were confirmed as the major controlling factors and material foundation for oil wells’ high and stable production. As indicated in this study, stress difference and gravel content are the major controlling factors in the formation of a complex fracture network.

Petrophysical Facies and Inferences on Permeability at Brothers Volcano, Kermadec Arc, Using Downhole Images and Petrophysical Data

Abstract Downhole data and cores collected during International Ocean Discovery Program (IODP) Expedition 376 at Brothers volcano, Kermadec arc, provide unprecedented, in situ views of volcanic facies and fluid pathways in an actively forming volcanogenic massive sulfide (VMS) ore deposit. Brothers volcano is a submarine caldera with extensive sea-floor hydrothermal alteration. Downhole data were collected in two holes: Hole U1530A at the NW Caldera and Hole U1528D at the Upper Cone. Textural analysis of microresistivity images in Hole U1530A provides a continuous image facies record that greatly improves findings based upon sporadic and partial (18%) core recovery. Between 90 and 214 meters below sea floor (mbsf), the heterogeneous image facies with local pattern variations is consistent with the volcaniclastic facies interpreted from cores. Between 232 and 445 mbsf, a volcanic facies was not recognizable in cores because of overprinting alteration, apart from five intervals of coherent lava flows that were less altered. Based on the fairly constant petrophysical data, Vp-porosity relationship, and presence of five to six coherent image facies intervals on the microresistivity image, we propose that the apparent volcaniclastic textures observed on cores and microresistivity images beneath 232 mbsf are dominantly lava flows. The change from volcaniclastic to dominant lava flow facies occurs over a transition zone (214–232 mbsf) where all petrophysical properties gradually change. In Hole U1528D, cores and petrophysical data show a similar transition from deep coherent lava flows to shallower, largely volcaniclastic sequences at ~270 mbsf. Down to 232 mbsf in Hole U1530A and 360 mbsf in Hole U1528D, the overall first-order downward decrease in porosity is interpreted to be caused by compaction and increased alteration intensity. Volcanic facies and fractures exert a second-order local control on petrophysical properties. Beneath 232 mbsf in Hole U1530A, the prolonged hydrothermal activity is inferred to have diminished local petrophysical property variations within the proposed lava flow-dominated rock package. High downhole fluid temperatures in Hole U1528D contrast with the moderate temperatures in Hole U1530A. Permeable zones show a mix of structural (inferred fault in Hole U1530A) and lithological controls in both holes. Some low-permeability layers and/or lithological interfaces possibly focus fluids laterally in higher-permeability layers, which may act as a trap for metal-rich fluids to form stratabound massive sulfides and deposits. Matrix is likely too low in permeability to conduct fluids but provides perfect conditions for the storage of supersaline brines. In Hole U1530A, located near active vents at the sea floor, the relatively low fluid temperature and the alteration overprint of moderate temperature demonstrate the high spatial and temporal variations at Brothers volcano. The implications of the new stratigraphy and controls on permeability proposed here for Brothers volcano include a better understanding of the following: (1) submarine volcanic eruption sequences, (2) permeability in active submarine volcanoes, and (3) the formation of volcanogenic massive sulfide deposits on (and near) the sea floor.

Semi-Supervised Learning–Based Petrophysical Facies Division and “Sweet Spot” Identification of Low-Permeability Sandstone Reservoir

The identification of the “sweet spot” of low-permeability sandstone reservoirs is a basic research topic in the exploration and development of oil and gas fields. Lithology identification, reservoir classification based on the pore structure and physical properties, and petrophysical facies classification are common methods for low-permeability reservoir classification, but their classification effect needs to be improved. The low-permeability reservoir is characterized by low rock physical properties, small porosity and permeability distribution range, and strong heterogeneity between layers. The seepage capacity and productivity of the reservoir vary considerably. Moreover, the logging response characteristics and resistivity value are similar for low-permeability reservoirs. In addition to physical properties and oil bearing, they are also affected by factors such as complex lithology, pore structure, and other factors, making it difficult for division of reservoir petrophysical facies and “sweet spot” identification. In this study, the logging values between low-porosity and -permeability reservoirs in the Paleozoic Es3 reservoir in the M field of the Bohai Sea, and between natural gamma rays and triple porosity reservoirs are similar. Resistivity is strongly influenced by physical properties, oil content, pore structure, and clay content, and the productivity difference is obvious. In order to improve the identification accuracy of “sweet spot,” a semi-supervised learning model for petrophysical facies division is proposed. The influence of lithology and physical properties on resistivity was removed by using an artificial neural network to predict resistivity R0 saturated with pure water. Based on the logging data, the automatic clustering MRGC algorithm was used to optimize the sensitive parameters and divide the logging facies to establish the unsupervised clustering model. Then using the divided results of mercury injection data, core cast thin layers, and logging faces, the characteristics of diagenetic types, pore structure, and logging response were integrated to identify rock petrophysical facies and establish a supervised identification model. A semi-supervised learning model based on the combination of “unsupervised supervised” was extended to the whole region training prediction for “sweet spot” identification, and the prediction results of the model were in good agreement with the actual results.

Petrophysical evaluation of sandstone gas reservoir using integrated well logs and core data for the Lower Cretaceous Kharita formation, Western Desert, Egypt

This study aims to evaluate Kharita gas reservoir to enhance the production. The increase in water-cut ratio reduces the left hydrocarbons’ amount behind pipe. Accurate determination of pore throats, pores connectivity and fluid distribution are central elements in improved reservoir description. The integration of core and logging data responses is often used to draw inferences about lithology, depositional sequences, facies, and fluid content. These inferences are based on petrophysical models utilizing correlations among tools’ responses as well as rocks and fluids properties. Upper Kharita Formation produces gas and condensate from the clastic sandstone in Badr-3 field, western desert of Egypt. It consists mainly of sandstone with shale intercalations. It is subdivided into three sub-units Kharita A, Kharita B and Kharita C that are in pressure communication. Hence, a new further investigation and review for the previously calculated GIIP (gas initially in place) was initiated. The results of this study yielded that the main uncertainty in the volumetric calculations was the petrophysical evaluation; subsequently, a new unconventional petrophysical evaluation approach was performed. The sands thickness in Upper Kharita Formation varies between more than 9 up and more than 61 m with average porosity values range between 0.08 and 0.17 PU while the average permeability values range between 1.89 and 696.66 mD. The average hydrocarbon saturation values range between 46 and 97%. The sands thickness in Upper Kharita Formation varies between more than 9 up and more than 61 m with average porosity values range between 0.08 and 0.17 PU while the average permeability values range between 1.89 and 696.66 mD. The average hydrocarbon saturation values range between 46 and 97%. Reservoir shale cutoff of 55% by using cross-plot between shale volume and porosity (Toby Darling concept) was utilized to discriminate the reservoir from non-reservoir sections. The porosity model was used to calculate reservoir porosity, using the density log. The Archie and saturation/height function models were used to calculate the water saturation and used to calibrate the water saturation in the transition zone. The porosity–permeability (POR-PERM) transform equation was used to estimate the reservoir connectivity (absolute permeability) for the four petrophysical facies (High Quality Reservoir, Moderate Quality Reservoir, Low Quality Reservoir and Highly Shale Reservoir). Core data have shown variations in reservoir quality parameters (porosity and permeability) from one well to the other. Integration of all the reservoir pressures indicated that there are different fluid types (oil, gas and water) in the Upper Kharita Formation level. The saturation/height function model was used to calibrate the saturation in the transition zone. The integration of geological core and geophysical log data helped to conduct a comprehensive petrophysical assessment of Upper Kharita Formation for a better estimation of the reservoir and to achieve a better understanding of the water encroachment in the Upper Kharita reservoir. The big challenge is the determination of the most correct model for calculating porosity, permeability and water saturation in this reservoir of different quality sand. The new petrophysical evaluation resulted in doubling the volumes in Upper Kharita reservoir and so a perforation campaign was performed to confirm the new volumetric calculations, which showed a good match with the model results. Hence, a new well was drilled targeting the low quality sand and found them with high pressure almost near virgin pressure.

Complex sandstone reservoir evaluation based on petrophysical facies classification: a case study of Huizhou Sag in the Pearl River Mouth Basin#br#

Complex sandstone reservoir evaluation based on petrophysical facies classification: a case study of Huizhou Sag in the Pearl River Mouth Basin#br#

A new approach to heterogeneity analysis in a highly complex carbonate reservoir by using borehole image and conventional log data

This study outstretches a new method to specify the heterogeneity of the studied reservoir by combining BHI (Borehole Image Log) and conventional log data for 4 existing wells. The first step to achieve this goal was utilizing borehole electrical images to assess the quantity of vugs/modals fraction and porosity distribution. Spectrum porosity was calculated through probabilistic analysis based on core and FMI data which has specified two general types of porosity in studied field as primary porosity (microporosities and intercrystalline) and secondary porosity (vugs). Due to irregular dispersion, non-categorization, and scattering of the porosity–permeability graph obtained from laboratory core data, next step was using petrophysical image facies prediction for generating an electrical facies to incorporate the different reservoir quality. This lithology (litho types) is produced by BHI analysis which reflects geological and petrophysical properties of the field. Then a porosity–permeability cross-plot has been made based on core data and produced facies codes which build in the previous step to check the validity of BHI Facies code extraction. Finally, heterogeneity analysis has been done through an innovative step-by-step workflow to determine spectrum porosity log which is divided into 6 categories/portions as Resistive, Matrix, Isolated, Connected, Bed boundaries and Fractures porosity.

Impacts of microfacies type on reservoir quality and pore fabric anisotropy of the Nubia sandstone in the central Eastern Desert, Egypt

The Nubia sandstone has a high potential for water and hydrocarbons exploration in its subsurface extensions. The present work aims to study the Nubia sandstone at Gebel Duwi in the central Eastern Desert to characterize its lithostratigraphic setting, microfacies types, and its petrophysical properties. In addition, impacts of mineral composition and diagenetic history of the Nubia sandstone on its storage and deliverability capacities and on its hydraulic and electric pore fabrics will be estimated. The Nubia sandstone is subdivided into three informal members: a lower trough cross‐bedded, coarse‐grained with little mud braided fluvial member; a middle planar cross‐bedded sandstone with ferruginous siltstone intercalations of meandered fluvial member; and an upper gypsiferous coarsening upward clastic coastal to deltaic sediments. The microfacies analysis indicated that six microfacies are present: quartz arenite, subarkose, arkose, sublitharenite, ferruginous greywacke, and ferruginous siltstone microfacies. Several types of diagenetic processes affected the Nubia sandstone; cementation, compaction, and pressure solution are the main reservoir quality‐reducing factors, whereas fracturing, dissolution, and leaching out are the most important reservoir quality‐enhancing factors. For the present study, helium and water porosities, air permeability, and electric resistivities (for plugs drilled in three perpendicular directions for each block sample) were measured. Petrophysically, the studied Nubia sandstone can be summed up into three petrophysical facies: Facies‐1 (quartz arenite and subarkose), Facies‐2 (ferruginous subarkose to greywacke), and Facies‐3 (sublitharenite and arkose). The slight ferruginous and clay content of the second facies has slightly increased the grain density values and reduced its storage capacity and deliverability. Porosity (∅He) values which are measured by the helium injection for the first and third facies samples are good to excellent (14.6 ≤ ∅He ≤ 28.9%), whereas their permeability (k) values are good to excellent (106 ≤ k ≤ 2231 md). The second facies is characterized by relatively less petrophysical potential than the other two facies (av. ∅He = 19.2% and av. k = 557 md). The hydraulic pore anisotropy (λk) of the studied three petrophysical facies is isotropic to moderately anisotropic (1.02 ≤ λk ≤ 1.96), whereas the electric pore anisotropy (λE) is less intensive (1.11 ≤ λE ≤ 1.61).

3D Geological Model For Khasib, Tanuma, and Sa'di formations of Halfaya Oil Field in Missan Governorate-Southern Iraq

A geological model is a spatial representation of the distribution of sediments and rocks in the subsurface. Where this study on Halfaya oil field; it is located in Missan governorate, 35 km southeast of the city of Amara. It is one of the main fields in Iraq because it is production high oil. This model contains the structure, and petrophysical properties (porosity, water saturation) in three directions. To build 3D geological models of petroleum reservoirs. Khasib, Tanuma, and Sa�di formations in Halfaya oil field have been divided into many layers depending on petrophysical properties and facies.

Reservoir assessment and quality discrimination of Kareem Formation using integrated petrophysical data, Southern Gulf of Suez, Egypt

Abstract The middle Miocene Kareem Formation represents one of the most important reservoirs in the Gulf of Suez, Egypt. Six boreholes locating throughout different structural trends in the Southern province of the Gulf of Suez, Egypt, were selected to study the reservoir properties of the Kareem Formation. The study depends on integrated data of well logs and routine core analysis as well as seismic data. The present study is an integrated study aims to study and discriminate the reservoir quality of the Kareem sequence using various statistical techniques. Testerman statistical approach and stratigraphic modified Lorenz (SML) plot were used in reservoir zonation. Principal Component Analysis (PCA) and cluster analysis were used to identify the electrofacies distribution within the studied formation. The well log data enabled characterizing the reservoir sequence into 6 electrofacies. The available core analyses data and core description for the Kareem Formation (850 feet cored interval) in Morgan oil field enabled more accurate discrimination for the reservoir sequence into 13 hydraulic flow units (7 of them are potential flow units). The studied sequence is differentiated into five lithologic-petrophysical facies; siltstone, dolomitic siltstone, dolomitic sandstone, sandstone and friable sandstone. The sandstone and friable sandstone facies are the best petrophysical facies. The dolomite content is the main key factor for reducing the reservoir quality of the studied Kareem sequence. From the core data, a total net pay thickness of 180 feet (mostly composed of sandstone with some siltstone intercalations) was assigned of fair quality (∅He = 14.7%, kH = 89.1 md, So = 28.8%, reservoir quality index ‘RQI’ = 0.60 μm, flow zone indicator ‘FZI’ = 3.81 μm, reservoir potential index ‘RPI’ = 2) with few streaks of good to very good potential. However, the siltstone samples have less petrophysical potential than the sandstones.

Modeling Poroelastic Wave Propagation in a Real 2-D Complex Geological Structure Obtained via Self-Organizing Maps

Two main stages of seismic modeling are geological model building and numerical computation of seismic response for the model. The quality of the computed seismic response is partly related to the type of model that is built. Therefore, the model building approaches become as important as seismic forward numerical methods. For this purpose, three petrophysical facies (sands, shales and limestones) are extracted from reflection seismic data and some seismic attributes via the clustering method called Self-Organizing Maps (SOM), which, in this context, serves as a geological model building tool. This model with all its properties is the input to the Optimal Implicit Staggered Finite Difference (OISFD) algorithm to create synthetic seismograms for poroelastic, poroacoustic and elastic media. The results show a good agreement between observed and 2-D synthetic seismograms. This demonstrates that the SOM classification method enables us to extract facies from seismic data and allows us to integrate the lithology at the borehole scale with the 2-D seismic data.

Geophysical data processing, rock property inversion, and geomechanical model building in a Midland Basin development project, Midland/Ector counties, Texas

The current state of the art for development of unconventional projects integrates geology, geophysics, and engineering into a comprehensive reservoir description. To that end, we have designed a comprehensive geophysical workflow to define the structure, stratigraphy, and rock mechanics of a stacked reservoir sequence in the west-central Midland Basin. The purpose of this paper is to describe the workflow. Portions of three seismic data sets were merged and processed anisotropically, preserving the full range of azimuths. Well control consisted of 35 compressional sonic logs, eight shear sonic logs, three sonic scanners, two lateral formation microimaging tools, and three cores. Nine newly drilled laterals are on production along with a large number of vertical legacy wells. Prestack inversion produced acoustic impedance, shear impedance, and density with good accuracy, although the compressional-to-shear-velocity ratio (VP/VS) was not stable. This volume was successfully recreated via neural net, which was also used to calculate total porosity and total water saturation volumes. Poisson's ratio, Young's modulus, and brittleness were created using standard equations and the neural net VP/VS volume. Structural attributes consisted of ant track, coherence, semblance, fault probability, and Kmin and Kmax curvature. The final step in this portion of the workflow was to create a high-resolution depth conversion velocity volume. A geocellular grid was built and populated with petrophysical volumes, lithology facies, and structural attributes. Principal stresses were incorporated by first establishing 1D mechanical earth models (MEMs) where appropriate log suites were available. These 1D MEMs were extended to a 3D MEM, although confidence in this volume is not high due to known pore pressure and closure stress reductions as a result of legacy vertical well production. Calibration incorporating new data as it becomes available is an ongoing task. An informative simulation using the MEM is frac models, which define the most optimal landing points for laterals. This MEM and the simulations it can perform have easily demonstrated the value of this integrated workflow.