Static Reservoir Models Research Articles

Three‐dimensional static reservoir modelling of Kareem sandstone reservoir, Tawilla Oil Field, at the southern region of Gulf of Suez, Egypt

The Gulf of Suez, which contains Egypt's oldest oil fields, is one of North Africa's most well‐known oil regions. There are more than 80 conventional oil fields in Egypt's Gulf of Suez, some of which have reservoirs that stretch back to the Precambrian and Quaternary. In close proximity to the southern entrance of the Gulf of Suez is the Tawilla West oilfield. The oil field Tawilla West is believed to consist of rotating fault blocks that descend in a south‐west direction. The main producing reservoirs are the Miocene section reservoirs, the Belayim and Kareem sandstones. The current research is focusing on the structural elements affecting this giant field to update the field structural model using the newly processed 3D seismic survey, the acquired data from newly drilled wells and the associated different logging techniques. The seismic information quality varied from poor to fair. The quality of the interpreted stratigraphic horizons and geological faults was mainly controlled by the seismic information quality. The research used seismic attribute analyses to improve interpretation and incorporate additional features, enabling better hydrocarbon potential identification and characterization of the reservoirs. Several geological structure contour maps and cross‐sections were generated to help in delineating and understanding the reservoir's extension. Based on the detailed correlation study, we were able to detect the faults that affected the structure of the Tawilla West field in detail, define their throw amounts and directions, and identify the missed sections across the studied area. This study introduces an updated model scenario to show the differences and their effect on the field development plan and recommendations. By examining subsurface geologic structural characteristics and evaluating petrophysical data, a 3D static reservoir model was created to resolve structural settings and hydrocarbon trapping, providing detailed information on the field and identifying new opportunities for future development. The research discovered that the updated detailed 3D structural model may support the Kareem Reservoir development plans and encourage drilling, workover and dynamic operations to assign development possibilities in the correct area. According to the established model, there are at least three options in the study's attic areas that might boost oil output and oil reserves for the field while avoiding further failures.

Integration of seismic and well log data for evaluating the upper cretaceous reservoirs of Horus oil field, Alamein Basin, Northern Western Desert, Egypt

Horus field stands out as one of the main oil-rich areas in Alamein Basin. Formation and evolution of Horus field were influenced by tectonic processes occurring from Jurassic to Cretaceous periods. The main objectives of this study are to characterize structural elements, identify hydrocarbon potential, and pinpoint promising zones within Early Cretaceous rocks for hydrocarbon accumulations in Horus reservoirs. By utilizing high-quality 2D seismic reflection sections enhanced with structural smoothing attribute, which help eliminate random noise while preserving structural details and horizon geometry, building a 3D structural model becomes facile. Analysis of well logging data provides a comprehensive understanding of Cretaceous reservoirs, simplified by 3D static reservoir modeling to depict their varying ranges and anisotropies. 3D structural model shows that the trap of Horus field is linked to a structural uplift in an ENE-WSW trend, bordered to north and south by sediment-filled lows. This anticlinal ridge within the model is segmented into blocks by a series of NW-SE normal faults. 3D static reservoir models reveal high-quality reservoirs in Upper Bahariya and Abu Roash (G) Dolomite, characterized by substantial thicknesses of net pay zones. 3D static modeling of Cretaceous reservoirs guides the oilfield development and optimizes production strategies in Horus area.

Seismic Inversion Techniques and Attribute Analysis for Accurate Well Placement in Offshore Niger Delta Basin, Nigeria

Before this study, simple methods like seismic attribute analysis have often been used for reservoir characterization with successes however, there is still the need to reduce exploration uncertainty to a negligible level and boost investors’ confidence. This study integrated seismic inversion with seismic attribute analysis to better characterize the reservoirs in MTW Field in deep-offshore Niger Delta Basin. Five (5) wells with complete suite of petrophysical logs, three-Dimensional (3-D) seismic data, checkshot and other well information were used. The well data were thoroughly quality checked, reservoirs were litho-stratigraphically delineated and used for petrophysical analysis across the wells. This was followed by seismic-to- well-tie, seismic interpretation, and seismic attribute analysis (Root Mean Square-RMS) generated using depth surface maps. 3-D static reservoir model and volumetric evaluation were carried out. Petrophysical properties were derived and distributed across the 3-D static model using sequential gaussian simulation algorithm to ascertain shale volume spread across the model. To improve the seismic resolution and reduce interpretation uncertainty at greater depth, model- based post-stack seismic inversion was performed to obtain acoustic impedance cube. Litho-stratigraphic and petrophysical analysis result revealed five reservoir sands (A, B, C, D, and E). Reservoir-A was seen to be more viable with a thickness of 13.42 m, high effective porosity of 27%, permeability of 3187.53 mD, low water saturation of 34% and low shale volume of 11% which are indication good reservoir quality and producibility. The seismic interpretation revealed thirty-one (31) growth and antithetic faults oriented in the NE-SW and NW-SE directions, respectively. The RMS result revealed high amplitude reflectivity which is a measure of zone of interest. Based on this, seven (7) prospects and three (3) leads were identified. The seismic inversion result shows a high level of accuracy with a correlation coefficient of 0.997; 0.997; 0.995; and 0.996 in MTW-001, MTW-003ST1, MTW-004ST1 and MTW-005 wells, respectively. The acoustic impedance successfully resolved and improved on the resolution of the seismic stacking velocity especially at reservoir layers and at depth deeper than 3600 ms. Acoustic impedance as a layer property has improved on the lateral and vertical resolutions of the data beyond what the usual seismic interval velocity could image thus, validating some of the prospects and leads identified in this study. This demonstrated that uncertainty can be reduced by a blend of RMS and seismic inversion in identifying reservoir for accurate placement of wells. It is therefore recommended that E and P operators should adopt the technique in their future hydrocarbon exploration endeavor in frontier and matured basins.

Multiphysics framework for cost-effective, long-term monitoring of carbon storage in a carbonate reservoir

We evaluate an effective and cost-efficient multiphysics approach for long-term monitoring of carbon storage in carbonate reservoirs. A key focus of our study is to advocate for a cost-effective, long-term monitoring strategy for carbon storage projects through the integration of surface and borehole multiphysics measurements. By leveraging technologies related to electromagnetic (EM) and gravity methods, we present a framework that addresses the monitoring of the dynamic behavior of injected CO2 over an extended period. The modeling setup integrates realistic static reservoir models with simulated dynamic saturation models to provide results as close as possible to real experimental conditions. Results demonstrate the high sensitivity of EM fields to changes in reservoir CO2 saturation, especially when surface-to-reservoir acquisition configurations are adopted. Additionally, gravity simulations, employing three-axis vector surface gravity measurements and borehole vector gravity, effectively capture density variations over time. Our proposed framework highlights the sequential use of seismic imaging for preinjection site characterization and the transition to EM and gravity surveys for continuous monitoring during and after the injection phases. The multiphysics simulation results performed on realistic carbon storage conditions contribute to the evolving landscape of effective geophysical CO2 monitoring technology for reliable long-term reservoir management.

Middle Eastern carbonate reservoirs – the critical impact of discrete zones of elevated permeability on reservoir performance

Abstract Middle Eastern carbonate petroleum reservoirs exhibit a range of heterogeneities which consist of variable combinations of primary stratigraphic and secondary diagenetic and structural characteristics. These produce diverse permeability architectures which can exert a profound influence on reservoir performance during secondary recovery. Of particular importance are laterally persistent discrete zones of elevated permeability (DZEP) that typically make up a volumetrically minor proportion of the reservoir yet show disproportionately high fluid inflow or outflow. The stratigraphic, diagenetic and structural origins of elevated permeability in Middle Eastern carbonate reservoirs are considered here and the consequences of such features for reservoir performance are discussed. The term DZEP denotes geological sources of elevated permeability at least an order of magnitude greater than background reservoir properties. Stratigraphically organized DZEP comprise coarse-grained layers, event beds or parasequence tops or bases in neritic or platform interior settings. Other origins include bioturbated layers, grainy clinothems and bed-scale, grain-size variations in shoal deposits. Diagenetic DZEP are typically dissolution horizons with mouldic and touching-vug pore networks or dolomitized intervals which often overprint stratigraphic DZEP. Structural DZEP include individual faults, fracture corridors and fracture concentrations related to mechanical stratigraphy. During production through natural pressure depletion, DZEP may dominate well productivity. Under secondary recovery, the same intervals may dominate inter-well fluid flow, causing flood conformance issues, cross-zone fluid movement, bypassed pay and earlier-than-expected water or gas breakthrough to production wells. Optimization of production and ultimate recovery rely on collecting the correct kinds of data at a sufficiently early stage in the reservoir characterization process to permit their inclusion in static and dynamic reservoir models.

3D Seismic Data Interpretation and Static Reservoir Modeling of Mamuniyat Formation, I and R Oil Fields, Murzuq Basin, Libya

3D Seismic Data Interpretation and Static Reservoir Modeling of Mamuniyat Formation, I and R Oil Fields, Murzuq Basin, Libya

The Selection of Optimal Gas Production Rate using Dynamic Reservoir Simulation in Field X

Indonesian government has targeted a gas production rate of 12 BSCFD by 2030 to balance the energy demands and the carbon emission reduction. To achieve this goal, a comprehensive evaluation of a gas field will be carried out regarding Recovery Factor, Stable Production Period (Plateau Time), Total Production Period, and Profits using simulation programme. Dynamic Reservoir Simulation is an integrated field development simulation that combines physics, mathematics, reservoir engineering, and computer programming to analyze and predict the wells performance or how fluid flows through reservoir rocks to the surface over time under various operating conditions. There are stages in the simulation include Reservoir Model Creation, Initialization, History Matching, and Production Performance Forecasting. This research is a continuation of static reservoir modeling research done by LEMIGAS, which started by reinitializing the model and ended by forecasting the production performance of field X using several gas production flowrate scenarios to find out the optimal gas production flowrate for Field X in ten years period. The simulation result showed that the best gas production flowrate for a ten-year period is 3 mmscfd. It gives 78,505% recovery factors, with cumulative profit of $40,915,872 USD over a plateau period of 6.6 years.

An effective multiphysics toolkit for lithium prospecting: From geophysics to the static reservoir model in the Pozuelos salt flat, Argentina

Energy transition drives the energy sector to renewable energy and electrification, being the critical minerals key players in the industrial development map. They comprise rare-earth elements and 35 other elements, including lithium, which holds 60% of its world reserves in the so-called lithium triangle located in Argentina, Bolivia, and Chile. The low electrical resistivities, variations in salt concentrations, low acoustic impedances, and dynamics of the hydrogeologic system make brine monitoring a complex geophysical exploratory problem. So, the objective is to find a suitable combination of geophysical techniques that fit the lithium exploration objectives, which are the characterization of the salt flat in-depth, fluid detection, basement delineation, definition of the main structures and main faults, and detection of semifresh water aquifers that contribute to its recharge and that are key to the reservoir water balance of an endorheic basin, which has the resource in solution. For this purpose, the evaluation of several prospecting methods in different salt flats was executed, concluding that full tensor magnetotellurics (MT), electrical resistivity tomography, and gravity comprise a toolkit that fits the objectives set. The methodology was validated at the Pozuelos salt flat. The results show that the fresh water-brines contact and the recharge system were well-defined and imaged by ERT. The full tensor MT detected two ultraconductive units: a shallower multilayer system saturated with brines, has a 400 m thickness, whereas the deeper unit has a 500 m thickness. Both MT and gravity characterized the basement, and gravity successfully delineated the main structures. The geophysical interpretation is in concordance with shallow and deep exploration wells. Finally, the integration of geophysical and well data allowed the construction of a 3D static reservoir model that finds the deepest basement area at approximately 900 m depth and discriminates eight lithofacies.

3D Static Reservoir Modeling: Case Study from the early Pliocene Kafr El-Sheikh Reservoir, Sapphire Gas and Condensate Field, Mediterranean Sea, Egypt

3D Static Reservoir Modeling: Case Study from the early Pliocene Kafr El-Sheikh Reservoir, Sapphire Gas and Condensate Field, Mediterranean Sea, Egypt

A machine-learning workflow to integrate high-resolution core-based facies into basin-scale stratigraphic models for the Wolfcamp and Third Bone Spring Sand, Delaware Basin

The characterization of subsurface reservoirs that are dominated by mudrock facies is hindered by the inherent heterogeneity and high degree of spatial variability typical of mudrock depositional systems. Subsurface reservoir properties that include porosity and permeability, fluid saturations, stratigraphic thicknesses of reservoir units, and source rock potential are ultimately controlled by the spatial distribution of sedimentary rock facies, which supports efforts to improve subsurface characterization workflows. Although core-based data provide direct measurements of rock attributes that are used to inform static reservoir models, capturing high-resolution core-based rock facies and downscaling these observations to tie to lower-resolution wireline logs remains a challenge. The effort to integrate core-based facies to reservoir-scale models is especially difficult when trying to capture thin-bedded heterogeneity that is common to mudrock systems. Herein, a workflow is developed and applied to visualize and integrate multivariate and spatially complex core-based data sets with wireline logs. Formation-specific core-based chemofacies training data sets are developed by integrating core descriptions with chemofacies clusters developed from high-resolution X-ray fluorescence core scanning. Core-based rock attribute data (e.g., XRD mineralogy, total porosity, and total organic matter content) are used to describe the chemofacies, providing a means to upscale low-resolution rock attribute measurements to high-resolution core-based chemofacies. Supervised core-based chemofacies training data sets are then used with neural network multiclass classification machine-learning tools to train triple combo wireline logs (gamma ray, deep resistivity, bulk density, and neutron porosity) to predict rock facies from wireline logs, providing a new approach to apply core-based facies classifications to wireline log studies. A basin-scale case study that applies this workflow is described for the Third Bone Spring Sand and units of the Wolfcamp Formation in the Delaware Basin of West Texas, United States.

New Insights into Fracture Porosity Estimations Using Machine Learning and Advanced Logging Tools

Fracture porosity is crucial for storage and production efficiency in fractured tight reservoirs. Geophysical image logs using resistivity measurements have traditionally been used for fracture characterization. This study aims to develop a novel, hybrid machine-learning method to predict fracture porosity using conventional well logs in the Ahnet field, Algeria. Initially, we explored an Artificial Neural Network (ANN) model for regression analysis. To overcome the limitations of ANN, we proposed a hybrid model combining Support Vector Machine (SVM) classification and ANN regression, resulting in improved fracture porosity predictions. The models were tested against logging data by combining the Machine Learning approach with advanced logging tools recorded in two wells. In this context, we used electrical image logs and the dipole acoustic tool, which allowed us to identify 404 open fractures and 231 closed fractures and, consequently, to assess the fracture porosity. The results were then fed into two machine-learning algorithms. Pure Artificial Neural Networks and hybrid models were used to obtain comprehensive results, which were subsequently tested to check the accuracy of the models. The outputs obtained from the two methods demonstrate that the hybridized model has a lower Root Mean Square Error (RMSE) than pure ANN. The results of our approach strongly suggest that incorporating hybridized machine learning algorithms into fracture porosity estimations can contribute to the development of more trustworthy static reservoir models in simulation programs. Finally, the combination of Machine Learning (ML) and well log analysis made it possible to reliably estimate fracture porosity in the Ahnet field in Algeria, where, in many places, advanced logging data are absent or expensive.

Integrating hydraulic flow unit concept and adaptive neuro-fuzzy inference system to accurately estimate permeability in heterogeneous reservoirs: Case study Sif Fatima oilfield, southern Algeria

Formation characteristics are a crucial requirement for reservoir modeling and they need to be constantly updated as new static and/or dynamic reservoir-property details become available. This improves reservoir interpretation while achieving a good history-matching for a better representation of the reservoir. This paper proposes a new method to estimate reservoir permeability in an oil-producing reservoir by applying two distinct cases: partially cored and completely non-cored wellbores. The approach involves the determination of hydraulic-flow units (HFU) using a flow-zone indicator (FZI) distribution to consider the dynamic aspect of the permeability, which strongly influences fluid flow through porous media. The FZI is clustered by normal probability analysis to identify the optimum number of HFU clusters. The estimated reservoir permeability is validated in static and dynamic reservoir models. For an accurate prediction of the FZI over the entire reservoir thickness, an adaptive-neuro-fuzzy-inference system (ANFIS) is developed using conventional well-log variables: (i) gamma-ray (GR), (ii) sonic (DT), (iii) density (RHOB), (iv) deep resistivity (DLL), and (iv) neutron porosity (NPHI) calibrated with the available core data. The static regression parameters root-mean-squared error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and R2adjusted are used to evaluate the performance of the ANFIS model in predicting FZI to determine HFU for estimating reservoir permeability. To validate this approach, a matching quality check of the downhole and reservoir pressure is carried out using a dynamic reservoir simulation model. Historical data collected from wellbore OPW-1 drilled in the Sif Fatima oilfield (Algeria) are used to evaluate the accuracy of the proposed permeability-prediction framework. The ANFIS model can accurately and efficiently predict the FZI exhibiting R2, R2adjusted, RMSE, and MAE of approximately (≈0.981, ≈0.979, ≈0.014, ≈4,600E-08)), (≈0.933, ≈0.891, ≈0.032, ≈1.615E-03), and (≈0.970, ≈0.968, ≈0.018, ≈2.381E-04)) for the training, testing and complete dataset, respectively. When applied to predict reservoir permeability, the Hydraulic Flow Unit concept was able to generate accurate results from the predicted FZI data by applying the modified Kozeny-Carman equation with, R2, RMSE and MAE values of approximately ≈0.9702, ≈0.969, ≈9.604, and ≈4.398, respectively. The calculated permeability was validated successfully at well and field levels. Additionally, it generated more accurate history matches of the well and the reservoir pressure trends than the previous permeability model, and consequently improved the reservoir modelling workflow.

Integrating seismic attributes, post-stack seismic inversion, and static modeling for assessing the Pliocene-Pleistocene hydrocarbon potentialities, Offshore North Sinai Field, Egypt

Optimal gas production from marginal fields vulnerable to tidal energetic effects is exceedingly challenging. Conventional seismic analysis is insufficient for a significant understanding of the geomorphological description, volumetric calculations, and microfacies analysis of the anticipated heterogeneous gas-bearing sands. New insights into the offshore North Sinai (ONS) Field based on quantitative and geostatistical interpretations integrated with physical and geometrical attribute analyses are boosted for reconstructing diverse-scale reservoir internal architecture.Deterministic band-limited inversion using acoustic impedance logs contributed to the initial knowledge of facies migration during tidal-dominated delta progradation and revealed four 2nd-order sequence boundaries dependent on P-velocity variations with depth as a function of rock elasticity, low impedance gas content of 2500–3000 kPa s/m and overburden compressional factors. Gradual boundaries confining reservoir bases due to spatiotemporal facies discrepancies triggered the implementation of stochastic algorism as a model norm for tracing thin-bedded and internal heterogeneous reservoirs with no predictable gas-water contacts across subtle dismantled areas. Stochastic inversion with intensive layering below −1 ms along with static property reservoir modeling allowed the evaluation of both fine and coarse-scale reservoirs discovered as extensions of injector-controlled stacked mouth bars, fan-lobes, and distributary channels crosscutting tidal mudflats of delta top facies associations within a system of spatially migrated clinoforms to the north. Stream avulsion-induced and accommodation space inconsistencies across syn-sedimentary faults caused the shifting of the Late Plio-Pleistocene clinoform depocenter northward and deltaic facies variation inside each individual sand system, not eustatic sea level fall.After calculating the biogenic gas reserves occupying each reservoir, the present strategy offers an additional seven broad gas prospects within four dismantled blocks, raising the ONS initial gas in place to 967.95 Bcf. In addition, it depicts five gas shoals emplaced at shallow depths, posing considerable drilling hazards. This paper workflow is the first of its kind in the Neogene section of the OSN Basin.

Improved delineation of the Gassum Formation reservoir zones using seismic impedance inversions: implications for exploiting the Stenlille aquifer gas storage facility as a CO 2 storage demonstration site, onshore Denmark

A quantitative seismic interpretation of the Gassum Formation at the onshore aquifer gas storage near the Danish town of Stenlille is presented with its implications for exploiting the gas storage facility as a potential CO 2 demonstration site. Our objective was to better outline the reservoir heterogeneity of the Gassum Formation based on a 3D seismic volume and 20 wells available from the Stenlille gas storage facility. We derived new absolute and relative P-impedance inversion products, which are useful for delineating lithological distributions of thin sandstone reservoirs and shaly beds within the Gassum Formation. Our results broadly agree with previously published seismic interpretations, and build upon these by the identification of deviations in parts of the Gassum interval that should be considered in any subsequent development of a static reservoir model. Hence, this work is an important contribution to the planning of drilling new CO 2 injection wells as part of the development and management of the Stenlille CO 2 storage demonstration site. Nevertheless, we also recommend a modern seismic reprocessing of the 3D seismic data combined with newly acquired 2D data from the Stenlille structure as input into further quantitative seismic interpretation studies to refine reservoir characterization and reduce associated uncertainties. Supplementary material: video is available at https://doi.org/10.6084/m9.figshare.c.6662413

3D Static Reservoir Modeling of the Bahariya Reservoirs in parts of the Shushan Basin, North Western Desert, Egypt

3D Static Reservoir Modeling of the Bahariya Reservoirs in parts of the Shushan Basin, North Western Desert, Egypt

Integrate inter–well connectivity data with static reservoir models based on Bayesian formalism

The inter-well connectivity calculated from reservoir dynamic production data reflects formation heterogeneity quantitatively. Currently, the calculated inter-well connectivity between pair wells is mainly used as a tool for water flood management but not for quantitative reservoir characterization. This study proposes an innovative, dynamic data integration workflow that can integrate inter-well connectivity with a static reservoir model. In the workflow, the first step is calculating the inter-well connectivity vectors from the reservoir pairwise injector and producer wells. The second step covers interpolation in the domain of interest. The third step is to update the permeability model based on the Bayesian updating method. The result of this study shows that integrating the calculated inter-well connectivity with the static models enhances model reliability and it also provides an insight to deeper geological understanding reflected from dynamic data integration in reservoir modeling.

Uncertainty Analysis of Reservoir Static Modelling: A Case Study of KMJ Oil Field

This case study explains the uncertainty of Original Oil in Place (OOIP) calculations in reservoir static modeling of KMJ Oil Field. This field consists of 4 (four) wells in an area of ± 600 acres with high heterogeneity, so in building a 3D Model, it is necessary to analyze the sensitivity and uncertainty of geological concepts, calculations of petrophysical properties, and fluid contact. The OOIP calculation uses a probabilistic method and determines reserves related to field development. The uncertainty analysis study begins by identifying the parameters with the most significant influence (Sensitivity Analysis) in calculating OOIP in the static reservoir model. To determine the ranking of reservoir uncertainty parameters, several geological, geophysical, and petrophysical factors in building a static model must be tested according to the method used in each parameter. The OOIP calculation in the static model is calculated into three scenario categories, namely low estimate (P10), base estimate (P50), and high estimate (P90). The combination of determining facies (shale volume) porosity, fluid contact, and the cut-off is a variable/parameter that is very influential in volumetric multi-scenario calculations (probabilistic method) in the KMJ Oil Field. The results of the uncertainty analysis of the KMJ Oil Field have a low OOIP estimate (P10) of 10.86 MMSTB, a base estimate (P50) of 11.49 MMSTB, and a high estimate (P90) of 12.01 MMSTB. Furthermore, the static model used for reservoir simulation (dynamic model) in the KMJ Oil Field is the base estimate model (P50) of 11.49 MMSTB.

Petroleum system analysis-conjoined 3D-static reservoir modeling in a 3-way and 4-way dip closure setting: Insights into petroleum geology of fluvio-marine deposits at BED-2 Field (Western Desert, Egypt)

Imperfect determination of petroleum system processes coincidence, entrapment and charge timing, along with reservoir heterogeneity can considerably cause high risks throughout exploration and development phases of petroleum. Therefore, a complete subsurface visualization of the petroleum system nature, elements and processes, is badly required. To this end, we corroborated, in this study, static reservoir modeling with petroleum system analysis workflows, to better characterize the Cenomanian fluvio-marine reservoir, sandstones of Bahariya Formation, at Bed-2 Field, Abu Gharadig basin (Western Desert, Egypt). We used dataset of 2D seismic profiles and well logs of eight wells. The geometry and property of the reservoir were acquired performing static reservoir geocellular modeling approach. The geohistory, timing of charge, migration pathways, and accumulation sites were identified performing 1D and 2D basin modeling approaches. Combining both approaches was aimed at identifying new petroleum prospect areas, and estimating the hydrocarbon volumes, that are the need for such poorly-defined petroleum systems area. As indicated by the constructed, robust, reservoir and 1D-2D basin models, an additional hydrocarbon prospect, to the north central part of Bed-2 Field, is proposed to be drilled during the further oilfield development phases of the area. This prospect has all features that adequately lead to reliable inferences regarding the ultimate petroleum potential of the area. • Reservoir modeling delineating geometry and property, with depositional facies analysis unlocking reservoir heterogeneity • Petroleum system analysis workflow defining the hydrocarbon charge timing, confirming the hydrocarbon potential of the area • Pixel-based stochastic simulation algorithms modeling the rock property. • Hydrocarbon volumes assessment, gas initially in place (GIIP) and stock-tank oil initially in place (STOIIP), in the area • Defining new prospect areas, justifying future oilfield development plans

3D static reservoir modelling to evaluate petroleum potential of Goru C-Interval sands in Sawan Gas Field, Pakistan

3D static reservoir modelling to evaluate petroleum potential of Goru C-Interval sands in Sawan Gas Field, Pakistan

Integrating facies analysis and geostatistical methods in an onshore oil field using petrophysical groups

Reservoir facies studies are of great importance in different stages of exploration and development of hydrocarbon fields. We have aimed to generate a reservoir facies model for the Asmari Formation in an onshore oil field located in southwest Iran. Input data for electrofacies (EF) clustering algorithms are used, which include gamma-ray (GR), density (RHOB), porosity, and sonic logs from four wells. We obtain the petrophysical group (PG) and EF class using core drill data (mercury injection capillary pressure) and well-logs analysis. The integration of PGs and log EF significantly decreases the uncertainty in reservoir modeling, which alternatively enhances field development decisions. We compare the multiresolution graph-based clustering (MRGC) and k-means clustering methods. EF clustering results find nine EF classes. We delineate high-quality reservoirs based on lower GR, RHOB, and high-porosity logs. Next, we use the clustering results in the static reservoir modeling process, using the sequential index simulation and indicator kriging methods. The comparison between the facies obtained models and existing drilling core data finds that the absolute percentage error of the MRGC algorithm is less than that of the k-means algorithm. The results obtained by this study can provide useful information for the development of hydrocarbon exploration plans in the studied oil field.