Deterministic and Stochastic Seismic Inversion techniques towards a better prediction for the reservoir distribution in NEAG-2 Field, north Western Desert, Egypt

Rashad, Osama; El-Barkooky, Ahmed N.; El-Araby, A.; El-Tonbary, M.

doi:10.1016/j.ejpe.2021.12.002

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…The stratigraphic grid for the discretization of the inversion process has a higher vertical resolution than the band-limited seismic data can provide. This obstacle is overcome by implementing frequency data beyond the seismic bandwidth to get broad-band inversion results with high vertical resolution of, e.g., 2 ms (Rashad et al 2022). Two typical input data sets for reconstruction of the missing frequency data are well log data and seismic velocity data.…”

Section: Seismic Amplitude Inversionmentioning

confidence: 99%

Porosity estimation of a geothermal carbonate reservoir in the German Molasse Basin based on seismic amplitude inversion

Wadas

Hartmann

2022

Geotherm Energy

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The Molasse Basin is one of the most promising areas for deep geothermal exploitation in Germany and the target horizon is the aquifer in the Upper Jurassic carbonates. Carbonate deposits can be very heterogeneous even over a small area due to diagenetic processes and varying depositional environments. The preferential targets for geothermal exploitation in carbonate deposits are fault zones, reef facies and karstified areas, since they are expected to act as hydraulically permeable zones due to high porosity and high permeability. Therefore, identifying these structures and characterizing, e.g., their internal porosity distribution are of high importance. This can be accomplished using 3D reflection seismic data. Besides structural information, 3D seismic surveys provide important reservoir properties, such as acoustic impedance, from which a porosity model can be derived. In our study area in Munich we carried out a seismic amplitude inversion to get an acoustic impedance model of the Upper Jurassic carbonate reservoir using a 3D seismic data set, a corresponding structural geological model, and logging data from six wells at the ‘Schäftlarnstraße’ geothermal site. The impedance model and porosity logs were than used to calculate a porosity model. The model shows a wide porosity range from 0 to 20% for the entire reservoir zone and the lithology along the wells reveals that dolomitic limestone has the highest porosities and calcareous dolomite has the lowest porosities. The study area is cut by a large W–E striking fault, the Munich Fault, and the footwall north of it shows higher porosities and more intense karstification than the hanging wall to the south. Considering the entire study area, an increase in porosity from east to west is observed. Furthermore, we identified a complex porosity distribution in reef buildups and pinnacle reefs. The reef cores have mostly low porosities of, e.g., < 3% and the highest porosities of up to 7 to 14% are observed at the reef caps and on the reef slopes. The reef slopes show a characteristic interfingering of the reef facies with the surrounding bedded facies, which indicates a syn-sedimentary reef development with slightly varying build up growth rates. We also assessed the reservoir quality with regard to porosity distribution and determined areas with moderate to good quality for geothermal exploitation by defining porosity evaluation levels. The porosity evaluation maps show that the carbonate rocks of Berriasian to Malm $$\zeta$$ ζ 1 are preferential targets for exploitation, especially in the footwall of the Munich Fault and to the west of the hanging wall, because these areas are characterized by high porosities due to intense karstification of bedded and massive facies, although the latter is mainly restricted to reef caps and reef slopes.

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Section: Seismic Amplitude Inversionmentioning

confidence: 99%

Porosity estimation of a geothermal carbonate reservoir in the German Molasse Basin based on seismic amplitude inversion

Wadas

Hartmann

2022

Geotherm Energy

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“…47 Conventional rock physics modeling (RPM) is carried out for multiple purposes, including the assessment of heterogeneities, detailed reservoir characterization, and predicting missing logs, mainly DTS. 5,7,8,25,52 The consistent and improved modeled DTS log was further utilized in seismic inversion techniques for upgraded reservoir characterization. Log data limitations of the IB, including poor RHOB along with the absence of DTS, are assessed using traditional RPM, incorporating petrophysical results, in situ parameters of the reservoir, and integration of petroelastic attributes.…”

Section: Methodsmentioning

confidence: 99%

“…Initially, conventional approaches, including seismic interpretation, petrophysical assessments, and fault-seal analysis, have been done in the research area. Additionally, meticulous approaches like deterministic and stochastic seismic inversions have been employed in various nearby producing fields to tackle these challenges. − However, their effectiveness in such complex geological settings often falls short, primarily due to their inability to handle missing data effectively, and the increased number of procedural steps may cause uncertainty. In recent years, the comparable use of advanced ML techniques has gained significant interest in subsurface imaging and reservoir characterization due to their enhanced result, robustness, and efficiency .…”

Section: Introductionmentioning

confidence: 99%

ResNet and CWT Fusion: A New Paradigm for Optimized Heterogeneous Thin Reservoir Evaluation

Akram,

Akhter,

et al. 2024

ACS Omega

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The endeavor to explore and characterize oil and gas reservoirs presents significant challenges due to the inherent heterogeneities that are further compounded by the existence of thin sand layers encapsulated in shale strata. This complexity is intensified by limited and low-resolution seismic data, missing critical well-log information, and inaccessible angle stack data. Conventional reservoir classification approaches have struggled to address these issues, primarily due to their limitations in handling missing data effectively and, hence, precise estimations. This study focuses on the characterization of thin, heterogeneous potential sands of the B-interval within the Lower Goru Formation, a proven gas reservoir in the Badin area. The reservoir sands with varying thicknesses are assessed in detail for their optimized description and field productions by handling challenges, including low seismic resolutions, heterogeneities, and missing data sets. An innovative solution is developed based on the integration of continuous wavelet transform (CWT) and machine learning (ML) techniques for the approximation of missing data sets, i.e., S-wave (DTS), along with enhanced elastic and petrophysical properties. The improved properties are augmented by the high resolution attained by CWT and captured variability more profoundly through the implication of residual neural networks (ResNet). The limitations of conventional approaches are harnessed by ML solutions that operate with limited input data and deliver significantly improved results in characterizing enigmatic thin sand reservoirs. The high-frequency petroelastic properties reliably determined the thin heterogeneous potential sand bodies and illuminated a channelized play fairway that can be tested for additional wells with low-risk involvement.

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“…Applying Petrel of 3D modeling software, to make a fault and structural model, facies-controlled model, and property model based on all available geological study results. Deterministic and stochastic reservoir modeling is the two most commonly used methodologies for reservoir modeling [22][23][24]. Linear interpolation, inverse square weighted average of distance, kriging method, and seismic 3 Geofluids reservoir prediction are among the techniques used in deterministic modeling [25].…”

Section: Methodsmentioning

confidence: 99%

Integrated Geologic Modeling of Fault-Block Reservoir: A Case Study of Ss Oil Field

et al. 2022

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The Ss oil field is found in the Turpan-Hami Basin’s Taipei Sag’s arc structural belt. This reservoir has a complicated character that has a significant impact on reservoir modeling and production prediction. This is a fault-block reservoir with ultralow permeability and low porosity that is divided by 57 faults. A static model was constructed by Petrel software based on reinterpretation of original log and core data and seismic information so as to clarify the spatial distribution of oil and water in the reservoir and to fit the development history of the later simulated reservoir. The integrated geological modeling approach is described in this work using the Ss reservoir as an example. A 3D structural model was built based on the spatial cutting relationship between the layer model and the fault, and the model’s quality was improved by breakpoint data, which more correctly depicted the structural properties of the research area. The lithofacies model was built within the restrictions of sedimentary facies using the sequential Gaussian simulation (SGS) stochastic modeling approach, which is paired with variogram data analysis to achieve the range value. To obtain the porosity and permeability model, the empirical formula of porosity and permeability, the SGS method, and the variation range value was input into the lithofacies model. It is important to note that the input lithofacies and property models have values of the same range. To gain the water saturation model, the distinct S w function formulas of the S 1 ~ S 4 layer derived from the J S w function were fed into the software. The NTG model was created according to the lower limit of porosity, which is 11%. The merging of detailed reservoir description and simulation led to the establishment of the Ss reservoir geological model. In the plane, the scale of the geological model has reached the meter level and decimeter level in the longitudinal direction. It also offers a framework for optimum reservoir modeling for complex fault-block reservoirs. This method improves the accuracy and precision of the model by reflecting the reservoir’s heterogeneity and the oil-water distribution. It could provide more details for future reservoir research such as fine reservoir simulation.

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Deterministic and Stochastic Seismic Inversion techniques towards a better prediction for the reservoir distribution in NEAG-2 Field, north Western Desert, Egypt

Cited by 6 publications

References 8 publications

Porosity estimation of a geothermal carbonate reservoir in the German Molasse Basin based on seismic amplitude inversion

Porosity estimation of a geothermal carbonate reservoir in the German Molasse Basin based on seismic amplitude inversion

ResNet and CWT Fusion: A New Paradigm for Optimized Heterogeneous Thin Reservoir Evaluation

Integrated Geologic Modeling of Fault-Block Reservoir: A Case Study of Ss Oil Field

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