Geostatistical Inversion in Carbonate Reservoirs to Map Reservoir Quality With High Predictability – A Case Study From Onshore Abu Dhabi

2021 ◽  
Author(s):  
S Al Naqbi ◽  
J Ahmed ◽  
J Vargas Rios ◽  
Y Utami ◽  
A Elila ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Pore Volume ◽  
Rock Physics ◽  
Static Model ◽  
Reservoir Quality ◽  
Reservoir Properties ◽  
Geostatistical Inversion ◽  
High Predictability ◽  
The Impact

Abstract The Thamama group of reservoirs consist of porous carbonates laminated with tight carbonates, with pronounced lateral heterogeneities in porosity, permeability, and reservoir thickness. The main objective of our study was mapping variations and reservoir quality prediction away from well control. As the reservoirs were thin and beyond seismic resolution, it was vital that the facies and porosity be mapped in high resolution, with a high predictability, for successful placement of horizontal wells for future development of the field. We established a unified workflow of geostatistical inversion and rock physics to characterize the reservoirs. Geostatistical inversion was run in static models that were converted from depth to time domain. A robust two-way velocity model was built to map the depth grid and its zones on the time seismic data. This ensured correct placement of the predicted high-resolution elastic attributes in the depth static model. Rock physics modeling and Bayesian classification were used to convert the elastic properties into porosity and lithology (static rock-type (SRT)), which were validated in blind wells and used to rank the multiple realizations. In the geostatistical pre-stack inversion, the elastic property prediction was constrained by the seismic data and controlled by variograms, probability distributions and a guide model. The deterministic inversion was used as a guide or prior model and served as a laterally varying mean. Initially, unconstrained inversion was tested by keeping all wells as blind and the predictions were optimized by updating the input parameters. The stochastic inversion results were also frequency filtered in several frequency bands, to understand the impact of seismic data and variograms on the prediction. Finally, 30 wells were used as input, to generate 80 realizations of P-impedance, S-impedance, Vp/Vs, and density. After converting back to depth, 30 additional blind wells were used to validate the predicted porosity, with a high correlation of more than 0.8. The realizations were ranked based on the porosity predictability in blind wells combined with the pore volume histograms. Realizations with high predictability and close to the P10, P50 and P90 cases (of pore volume) were selected for further use. Based on the rock physics analysis, the predicted lithology classes were associated with the geological rock-types (SRT) for incorporation in the static model. The study presents an innovative approach to successfully integrate geostatistical inversion and rock physics with static modeling. This workflow will generate seismically constrained high-resolution reservoir properties for thin reservoirs, such as porosity and lithology, which are seamlessly mapped in the depth domain for optimized development of the field. It will also account for the uncertainties in the reservoir model through the generation of multiple equiprobable realizations or scenarios.

Unsupervised machine learning using 3D seismic data applied to reservoir evaluation and rock type identification

2021 ◽  
pp. 1-69
Author(s):  
Marwa Hussein ◽  
Robert R. Stewart ◽  
Deborah Sacrey ◽  
Jonny Wu ◽  
Rajas Athale
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Rock Type ◽  
Seismic Attributes ◽  
Reservoir Quality ◽  
3D Seismic ◽  
Reservoir Properties ◽  
Unsupervised Machine Learning ◽  
Amplitude Data ◽  
3D Seismic Data

Net reservoir discrimination and rock type identification play vital roles in determining reservoir quality, distribution, and identification of stratigraphic baffles for optimizing drilling plans and economic petroleum recovery. Although it is challenging to discriminate small changes in reservoir properties or identify thin stratigraphic barriers below seismic resolution from conventional seismic amplitude data, we have found that seismic attributes aid in defining the reservoir architecture, properties, and stratigraphic baffles. However, analyzing numerous individual attributes is a time-consuming process and may have limitations for revealing small petrophysical changes within a reservoir. Using the Maui 3D seismic data acquired in offshore Taranaki Basin, New Zealand, we generate typical instantaneous and spectral decomposition seismic attributes that are sensitive to lithologic variations and changes in reservoir properties. Using the most common petrophysical and rock typing classification methods, the rock quality and heterogeneity of the C1 Sand reservoir are studied for four wells located within the 3D seismic volume. We find that integrating the geologic content of a combination of eight spectral instantaneous attribute volumes using an unsupervised machine-learning algorithm (self-organizing maps [SOMs]) results in a classification volume that can highlight reservoir distribution and identify stratigraphic baffles by correlating the SOM clusters with discrete net reservoir and flow-unit logs. We find that SOM classification of natural clusters of multiattribute samples in the attribute space is sensitive to subtle changes within the reservoir’s petrophysical properties. We find that SOM clusters appear to be more sensitive to porosity variations compared with lithologic changes within the reservoir. Thus, this method helps us to understand reservoir quality and heterogeneity in addition to illuminating thin reservoirs and stratigraphic baffles.

Diagenetic Evolution and Reservoir Quality of Sandstones in the North Alpine Foreland Basin: A Microscale Approach

2015 ◽  
Vol 21 (5) ◽  
pp. 1123-1137 ◽  
Author(s):  
Doris Gross ◽  
Marie-Louise Grundtner ◽  
David Misch ◽  
Martin Riedl ◽  
Reinhard F. Sachsenhofer ◽  
...  
Keyword(s):  
Foreland Basin ◽  
Gas Production ◽  
Coarse Grained ◽  
Reservoir Quality ◽  
Reservoir Properties ◽  
The North ◽  
North Alpine Foreland Basin ◽  
Alpine Foreland ◽  
Alpine Foreland Basin ◽  
The Impact

AbstractSiliciclastic reservoir rocks of the North Alpine Foreland Basin were studied focusing on investigations of pore fillings. Conventional oil and gas production requires certain thresholds of porosity and permeability. These parameters are controlled by the size and shape of grains and diagenetic processes like compaction, dissolution, and precipitation of mineral phases. In an attempt to estimate the impact of these factors, conventional microscopy, high resolution scanning electron microscopy, and wavelength dispersive element mapping were applied. Rock types were established accordingly, considering Poro/Perm data. Reservoir properties in shallow marine Cenomanian sandstones are mainly controlled by the degree of diagenetic calcite precipitation, Turonian rocks are characterized by reduced permeability, even for weakly cemented layers, due to higher matrix content as a result of lower depositional energy. Eocene subarkoses tend to be coarse-grained with minor matrix content as a result of their fluvio-deltaic and coastal deposition. Reservoir quality is therefore controlled by diagenetic clay and minor calcite cementation.Although Eocene rocks are often matrix free, occasionally a clay mineral matrix may be present and influence cementation of pores during early diagenesis. Oligo-/Miocene deep marine rocks exhibit excellent quality in cases when early cement is dissolved and not replaced by secondary calcite, mainly bound to the gas–water contact within hydrocarbon reservoirs.

From Seismic to Reservoir Properties With Geostatistical Inversion

1999 ◽  
Vol 2 (04) ◽  
pp. 334-340 ◽  
Author(s):  
Philippe Lamy ◽  
P.A. Swaby ◽  
P.S. Rowbotham ◽  
Olivier Dubrule ◽  
A. Haas
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
A Priori ◽  
Three Dimensional ◽  
Well Logs ◽  
Inversion Method ◽  
Inversion Process ◽  
Reservoir Properties ◽  
Stratigraphic Model ◽  
Geostatistical Inversion

Summary The methodology presented in this paper incorporates seismic data, geological knowledge and well logs to produce models of reservoir parameters and uncertainties associated with them. A three-dimensional (3D) seismic dataset is inverted within a geological and stratigraphic model using the geostatistical inversion technique. Several reservoir-scale acoustic impedance blocks are obtained and quantification of uncertainty is determined by computing statistics on these 3D blocks. Combining these statistics with the kriging of the reservoir parameter well logs allows the transformation of impedances into reservoir parameters. This combination is similar to performing a collocated cokriging of the acoustic impedances. Introduction Our geostatistical inversion approach is used to invert seismic traces within a geological and stratigraphic model. At each seismic trace location, a large number of acoustic impedance (AI) traces are generated by conditional simulation, and a local objective function is minimized to find the trace that best fits the actual seismic trace. Several three-dimensional (3D) AI realizations are obtained, all of which are constrained by both the well logs and seismic data. Statistics are then computed in each stratigraphic cell of the 3D results to quantify the nonuniqueness of the solution and to summarize the information provided by individual realizations. Finally, AI are transformed into other reservoir parameters such as Vshale through a statistical petrophysical relationship. This transformation is used to map Vshale between wells, by combining information derived from Vshale logs with information derived from AI blocks. The final block(s) can then be mapped from the time to the depth domain and used for building the flow simulation models or for defining reservoir characterization maps (e.g., net to gross, hydrocarbon pore volume). We illustrate the geostatistical inversion method with results from an actual case study. The construction of the a-priori model in time, the inversion, and the final reservoir parameters in depth are described. These results show the benefit of a multidisciplinary approach, and illustrate how the geostatistical inversion method provides clear quantification of uncertainties affecting the modeling of reservoir properties between wells. Methodology The Geostatistical Inversion Approach. This methodology was introduced by Bortoli et al.1 and Haas and Dubrule.2 It is also discussed in Dubrule et al.3 and Rowbotham et al.4 Its application on a synthetic case is described in Dubrule et al.5 A brief review of the method will be presented here, emphasizing how seismic data and well logs are incorporated into the inversion process. The first step is to build a geological model of the reservoir in seismic time. Surfaces are derived from sets of picks defining the interpreted seismic. These surfaces are important sincethey delineate the main layers of the reservoir and, as we will see below, the statistical model associated with these layers, andthey control the 3D stratigraphic grid construction. The structure of this grid (onlap, eroded, or proportional) depends on the geological context. The maximum vertical discretization may be higher than that of the seismic, typically from 1 to 4 milliseconds. The horizontal discretization is equal to the number of seismic traces to invert in each direction (one trace per cell in map view). Raw AI logs at the wells have to be located within this stratigraphic grid since they will be used as conditioning data during the inversion process. It is essential that well logs should be properly calibrated with the seismic. This implies that a representative seismic wavelet has been matched to the wells, by comparing the convolved reflectivity well log response with the seismic response at the same location. This issue is described more fully in Rowbotham et al.4 Geostatistical parameters are determined by using both the wells and seismic data. Lateral variograms are computed from the seismic mapped into the stratigraphic grid. Well logs are used to both give an a priori model (AI mean and standard deviation) per stratum and to compute vertical variograms. The geostatistical inversion process can then be started. A random path is followed by the simulation procedure, and at each randomly drawn trace location AI trace values can be generated by sequential Gaussian simulation (SGS). A large number of AI traces are generated at the same location and the corresponding reflectivities are calculated. After convolution with the wavelet, the AI trace that leads to the best fit with the actual seismic is kept and merged with the wells and the previously simulated AI traces. The 3D block is therefore filled sequentially, trace after trace (see Fig. 1). It is possible to ignore the seismic data in the simulation process by generating only one trace at any (X, Y) location and automatically keeping it as "the best one." In this case, realizations are only constrained by the wells and the geostatistical model (a-priori parameters and variograms).

Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion

Geophysics ◽  
2010 ◽  
Vol 75 (3) ◽  
pp. O21-O37 ◽  
Author(s):  
Dario Grana ◽  
Ernesto Della Rossa
Keyword(s):  
Seismic Data ◽  
Measurement Errors ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Joint Estimation ◽  
Physical Models ◽  
Inversion Method ◽  
Petrophysical Properties ◽  
Reservoir Properties ◽  
Statistical Rock Physics

A joint estimation of petrophysical properties is proposed that combines statistical rock physics and Bayesian seismic inversion. Because elastic attributes are correlated with petrophysical variables (effective porosity, clay content, and water saturation) and this physical link is associated with uncertainties, the petrophysical-properties estimation from seismic data can be seen as a Bayesian inversion problem. The purpose of this work was to develop a strategy for estimating the probability distributions of petrophysical parameters and litho-fluid classes from seismics. Estimation of reservoir properties and the associated uncertainty was performed in three steps: linearized seismic inversion to estimate the probabilities of elastic parameters, probabilistic upscaling to include the scale-changes effect, and petrophysical inversion to estimate the probabilities of petrophysical variables andlitho-fluid classes. Rock-physics equations provide the linkbetween reservoir properties and velocities, and linearized seismic modeling connects velocities and density to seismic amplitude. A full Bayesian approach was adopted to propagate uncertainty from seismics to petrophysics in an integrated framework that takes into account different sources of uncertainty: heterogeneity of the real data, approximation of physical models, measurement errors, and scale changes. The method has been tested, as a feasibility step, on real well data and synthetic seismic data to show reliable propagation of the uncertainty through the three different steps and to compare two statistical approaches: parametric and nonparametric. Application to a real reservoir study (including data from two wells and partially stacked seismic volumes) has provided as a main result the probability densities of petrophysical properties and litho-fluid classes. It demonstrated the applicability of the proposed inversion method.

Reservoir properties prediction integrating controlled-source electromagnetic, prestack seismic, and well-log data using a rock-physics framework: Case study in the Hoop Area, Barents Sea, Norway

2017 ◽  
Vol 5 (2) ◽  
pp. SE43-SE60 ◽  
Author(s):  
Pedro Alvarez ◽  
Amanda Alvarez ◽  
Lucy MacGregor ◽  
Francisco Bolivar ◽  
Robert Keirstead ◽  
...  
Keyword(s):  
Seismic Data ◽  
Barents Sea ◽  
Rock Physics ◽  
Positive Outcome ◽  
Rock Properties ◽  
Integrated Analysis ◽  
Reservoir Properties ◽  
Data Set ◽  
Controlled Source ◽  
Rotation Scheme

We have developed an example from the Hoop Area of the Barents Sea showing a sequential quantitative integration approach to integrate seismic and controlled-source electromagnetic (CSEM) attributes using a rock-physics framework. The example illustrates a workflow to address the challenges of multiphysics and multiscale data integration for reservoir characterization purposes. A data set consisting of 2D GeoStreamer seismic and towed streamer electromagnetic data that were acquired concurrently in 2015 by PGS provide the surface geophysical measurements that we used. Two wells in the area — Wisting Central (7324/8-1) and Wisting Alternative (7324/7-1S) — provide calibration for the rock-physics modeling and the quantitative integrated analysis. In the first stage of the analysis, we invert prestack seismic and CSEM data separately for impedance and anisotropic resistivity, respectively. We then apply the multi-attribute rotation scheme (MARS) to estimate rock properties from seismic data. This analysis verified that the seismic data alone cannot distinguish between commercial and noncommercial hydrocarbon saturation. Therefore, in the final stage of the analysis, we invert the seismic and CSEM-derived properties within a rock-physics framework. The inclusion of the CSEM-derived resistivity information within the inversion approach allows for the separation of these two possible scenarios. Results reveal excellent correlation with known well outcomes. The integration of seismic, CSEM, and well data predicts very high hydrocarbon saturations at Wisting Central and no significant saturation at Wisting Alternative, consistent with the findings of each well. Two further wells were drilled in the area and used as blind tests in this case: The slightly lower saturation predicted at Hanssen (7324/7-2) is related to 3D effects in the CSEM data, but the positive outcome of the well is correctly predicted. At Bjaaland (7324/8-2), although the seismic indications are good, the integrated interpretation result predicts correctly that this well was unsuccessful.

A rock physics strategy for quantifying uncertainty in common hydrocarbon indicators

Geophysics ◽  
1998 ◽  
Vol 63 (6) ◽  
pp. 1997-2008 ◽  
Author(s):  
Gary Mavko ◽  
Tapan Mukerji
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Pore Fluid ◽  
Fluid Substitution ◽  
Seismic Velocities ◽  
S Wave ◽  
Seismic Attribute ◽  
The Impact ◽  
Quantifying Uncertainty ◽  
Range Of Values

We present a strategy for quantifying uncertainties in rock physics interpretations by combining statistical techniques with deterministic rock physics relations derived from the laboratory and theory. A simple example combines Gassmann’s deterministic equation for fluid substitution with statistics inferred from log, core, and seismic data to detect hydrocarbons from observed seismic velocities. The formulation identifies the most likely pore fluid modulus corresponding to each observed seismic attribute and the uncertainty that arises because of natural variability in formation properties, in addition to the measurement uncertainties. We quantify the measure of information in terms of entropy and show the impact of additional data about S-wave velocity on the uncertainty of the hydrocarbon indicator. In some cases, noisy S data along with noisy P data can convey more information than perfect P data alone, while in other cases S data do not reduce the uncertainty. We apply the formulation to a well log example for detecting the most likely pore fluid and quantifying the associated uncertainty from observed sonic and density logs. The formulation offers a convenient way to implement deterministic fluid substitution equations in the realistic case when natural geologic variations cause the reference porosity and velocity to span a range of values.

scholarly journals Impact of Diagenesis on the Reservoir Properties of the Cretaceous Sandstones in the Southern Bredasdorp Basin, Offshore South Africa

Minerals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 757
Author(s):  
Temitope Love Baiyegunhi ◽  
Kuiwu Liu ◽  
Oswald Gwavava ◽  
Christopher Baiyegunhi
Keyword(s):  
Oil And Gas ◽  
Gas Production ◽  
Reservoir Quality ◽  
Reservoir Properties ◽  
Oil And Gas Production ◽  
Thin Sections ◽  
Hydrocarbon Reservoir ◽  
Porosity And Permeability ◽  
The Impact

The Cretaceous sandstone in the Bredasdorp Basin is an essential potential hydrocarbon reservoir. In spite of its importance as a reservoir, the impact of diagenesis on the reservoir quality of the sandstones is almost unknown. This study is undertaken to investigate the impact of digenesis on reservoir quality as it pertains to oil and gas production in the basin. The diagenetic characterization of the reservoir is based on XRF, XRD SEM + EDX, and petrographic studies of 106 thin sections of sandstones from exploration wells E-AH1, E-AJ1, E-BA1, E-BB1 and E-D3 in the basin. The main diagenetic processes that have affected the reservoir quality of the sandstones are cementation by authigenic clay, carbonate and silica, growth of authigenic glauconite, dissolution of minerals and load compaction. Based on the framework grain–cement relationships, precipitation of the early calcite cement was either accompanied or followed up by the development of partial pore-lining and pore-filling clay cements, particularly illite. This clay acts as pore choking cement, which reduces porosity and permeability of the reservoir rocks. The scattered plots of porosity and permeability versus cement + clays show good inverse correlations, suggesting that the reservoir quality is mainly controlled by cementation and authigenic clays.

Predicting reservoir quality in the Bakken Formation, North Dakota, using petrophysics and 3C seismic data

2020 ◽  
Vol 8 (4) ◽  
pp. T851-T868
Author(s):  
Andrea G. Paris ◽  
Robert R. Stewart
Keyword(s):  
North Dakota ◽  
Seismic Data ◽  
Rock Physics ◽  
P Wave ◽  
Well Logs ◽  
Reservoir Quality ◽  
Bakken Formation ◽  
S Wave ◽  
Converted Wave ◽  
Wave Velocities

Combining rock-property analysis with multicomponent seismic imaging can be an effective approach for reservoir quality prediction in the Bakken Formation, North Dakota. The hydrocarbon potential of shale is indicated on well logs by low density, high gamma-ray response, low compressional-wave (P-wave) and shear-wave (S-wave) velocities, and high neutron porosity. We have recognized the shale intervals by cross plotting sonic velocities versus density. Intervals with total organic carbon (TOC) content higher than 10 wt% deviate from lower TOC regions in the density domain and exhibit slightly lower velocities and densities (<2.30 g/cm3). We consider TOC to be the principal factor affecting changes in the density and P- and S-wave velocities in the Bakken shales, where VP/ VS ranges between 1.65 and 1.75. We generate the synthetic seismic data using an anisotropic version of the Zoeppritz equations, including estimated Thomsen’s parameters. For the tops of the Upper and Lower Bakken, the amplitude shows a negative intercept and a positive gradient, which corresponds to an amplitude variation with offset of class IV. The P-impedance error decreases by 14% when incorporating the converted-wave information in the inversion process. A statistical approach using multiattribute analysis and neural networks delimits the zones of interest in terms of P-impedance, density, TOC content, and brittleness. The inverted and predicted results show reasonable correlations with the original well logs. The integration of well log analysis, rock physics, seismic modeling, constrained inversions, and statistical predictions contributes to identifying the areas of highest reservoir quality within the Bakken Formation.

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