An Integrated biostratigraphic, seismic reservoir characterization and numerical stratigraphic forward modelling approach to imaging drowned carbonate platforms: a case study from eastern Indonesia

2020 ◽  
pp. SP509-2019-88
Author(s):  
David P. Gold ◽  
Francois Baillard ◽  
Rajat Rathore ◽  
Zhengmin Zhang ◽  
Safrin Arbi
Keyword(s):  
Reservoir Characterization ◽  
Model Building ◽  
Carbonate Platform ◽  
New Guinea ◽  
Velocity Model ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Forward Modelling ◽  
Carbonate Platforms ◽  
Seismic Reservoir

AbstractThe New Guinea Limestone Group was deposited across much of New Guinea, including the Indonesian provinces of West Papua and Papua, as part of a widespread shallow-water carbonate platform during the Paleogene and Neogene. This platform was drowned beneath deeper-water strata from the Middle to Late Miocene. Review of biostratigraphic and seismic data from the Aru Basin, offshore New Guinea, reveals a drowning succession c. 600 m thick deposited during a drowning event that lasted around 4 Ma. The objective of this study was to create a well-to-seismic tie from a single well in the study area using biostratigraphic, seismic and log data. The well-to-seismic tie was built to constrain a new velocity model to better image the drowned carbonate platform and understand the reservoir potential of the drowning succession in the zone of interest using two complimentary techniques: seismic reservoir characterization and numerical stratigraphic forward modelling. The well-to-seismic tie was achieved by matching significant biostratigraphic events, such as unconformities, with seismic horizons using stratigraphy-to-seismic. Modern stratigraphic and seismic reservoir characterization techniques, including stratigraphy-to-seismic, numerical forward modelling, velocity model building, rock physics and seismic inversion, were applied to predict rock properties such as lithology and porosity within the drowning succession.

Introduction to this special section: Rock physics

2019 ◽  
Vol 38 (5) ◽  
pp. 332-332
Author(s):  
Yongyi Li ◽  
Lev Vernik ◽  
Mark Chapman ◽  
Joel Sarout
Keyword(s):  
Reservoir Characterization ◽  
Model Building ◽  
Seismic Analysis ◽  
Focal Point ◽  
Special Section ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Reservoir Properties ◽  
Model Studies ◽  
Experimental Rock

Rock physics links the physical properties of rocks to geophysical and petrophysical observations and, in the process, serves as a focal point in many exploration and reservoir characterization studies. Today, the field of rock physics and seismic petrophysics embraces new directions with diverse applications in estimating static and dynamic reservoir properties through time-variant mechanical, thermal, chemical, and geologic processes. Integration with new digital and computing technologies is gradually gaining traction. The use of rock physics in seismic imaging, prestack seismic analysis, seismic inversion, and geomechanical model building also contributes to the increase in rock-physics influence. This special section highlights current rock-physics research and practices in several key areas, namely experimental rock physics, rock-physics theory and model studies, and the use of rock physics in reservoir characterizations.

Fluids and lithofacies prediction based on integration of well-log data and seismic inversion: a machine learning approach

Geophysics ◽  
2021 ◽  
pp. 1-67
Author(s):  
Luanxiao Zhao ◽  
Caifeng Zou ◽  
Yuanyuan Chen ◽  
Wenlong Shen ◽  
Yirong Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Domain Knowledge ◽  
Reservoir Characterization ◽  
Model Building ◽  
Imbalanced Data ◽  
Seismic Inversion ◽  
Well Log ◽  
Spatial Constraints ◽  
Imbalanced Data Sets

Seismic prediction of fluid and lithofacies distributions is of great interest to reservoir characterization, geological model building, and flow unit delineation. Inferring fluids and lithofacies from seismic data under the framework of machine learning is commonly subject to issues of limited features, imbalanced data sets, and spatial constraints. As a consequence, an XGBoost based workflow, which takes feature engineering, data balancing, and spatial constraints into account, is proposed to predict the fluid and lithofacies distribution by integrating well-log and seismic data. The constructed feature set based on simple mathematical operations and domain knowledge outperforms the benchmark group consisting of conventional elastic attributes of P-impedance and Vp/Vs ratio. A radial basis function characterizing the weights of training samples according to the distances from the available wells to the target region is developed to impose spatial constraints on the model training process, significantly improving the prediction accuracy and reliability of gas sandstone. The strategy combining the synthetic minority oversampling technique (SMOTE) and spatial constraints further increases the F1 score of gas sandstone and also benefits the overall prediction performance of all the facies. The application of the combined strategy on prestack seismic inversion results generates a more geologically reasonable spatial distribution of fluids, thus verifying the robustness and effectiveness of the proposed workflow.

Stuck between a rock and a reflection: A tutorial on low-frequency models for seismic inversion

2017 ◽  
Vol 5 (2) ◽  
pp. B17-B27 ◽  
Author(s):  
Mark Sams ◽  
David Carter
Keyword(s):  
Elastic Properties ◽  
Model Building ◽  
Seismic Velocity ◽  
Low Frequency ◽  
Rock Physics ◽  
Frequency Component ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Seismic Velocities ◽  
Frequency Model

Predicting the low-frequency component to be used for seismic inversion to absolute elastic rock properties is often problematic. The most common technique is to interpolate well data within a structural framework. This workflow is very often not appropriate because it is too dependent on the number and distribution of wells and the interpolation algorithm chosen. The inclusion of seismic velocity information can reduce prediction error, but it more often introduces additional uncertainties because seismic velocities are often unreliable and require conditioning, calibration to wells, and conversion to S-velocity and density. Alternative techniques exist that rely on the information from within the seismic bandwidth to predict the variations below the seismic bandwidth; for example, using an interpretation of relative properties to update the low-frequency model. Such methods can provide improved predictions, especially when constrained by a conceptual geologic model and known rock-physics relationships, but they clearly have limitations. On the other hand, interpretation of relative elastic properties can be equally challenging and therefore interpreters may find themselves stuck — unsure how to interpret relative properties and seemingly unable to construct a useful low-frequency model. There is no immediate solution to this dilemma; however, it is clear that low-frequency models should not be a fixed input to seismic inversion, but low-frequency model building should be considered as a means to interpret relative elastic properties from inversion.

Seismic reservoir characterization of Bone Spring and Wolfcamp Formations in the Delaware Basin — A case study: Part 1

2020 ◽  
Vol 8 (4) ◽  
pp. T927-T940
Author(s):  
Satinder Chopra ◽  
Ritesh Kumar Sharma ◽  
James Keay
Keyword(s):  
Reservoir Characterization ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Delaware Basin ◽  
Seismic Reservoir ◽  
Data Conditioning ◽  
Inversion Analysis ◽  
Sweet Spots

The Delaware and Midland Basins are multistacked plays with production being drawn from different zones. Of the various prospective zones in the Delaware Basin, the Bone Spring and Wolfcamp Formations are the most productive and thus are the most drilled zones. To understand the reservoirs of interest and identify the hydrocarbon sweet spots, a 3D seismic inversion project was undertaken in the northern part of the Delaware Basin in 2018. We have examined the reservoir characterization exercise for this dataset in two parts. In addition to a brief description of the geology, we evaluate the challenges faced in performing seismic inversion for characterizing multistacked plays. The key elements that lend confidence in seismic inversion and the quantitative predictions made therefrom are well-to-seismic ties, proper data conditioning, robust initial models, and adequate parameterization of inversion analysis. We examine the limitations of a conventional approach associated with these individual steps and determine how to overcome them. Later work will first elaborate on the uncertainties associated with input parameters required for executing rock-physics analysis and then evaluate the proposed robust statistical approach for defining the different lithofacies.

scholarly journals A Review of Stratigraphic Foward Models (Sfm) for Carbonate Platform

2018 ◽  
Vol 7 (4.35) ◽  
pp. 143
Author(s):  
Redwan Rosli ◽  
Michael C. Poppelreiter ◽  
Siti Nur Fathiyah Jamaludin
Keyword(s):  
Review Paper ◽  
Carbonate Platform ◽  
Hydrocarbon Exploration ◽  
Forward Modelling ◽  
Carbonate Facies ◽  
Carbonate Platforms ◽  
Key Factors ◽  
Carbonate System ◽  
Depositional Processes ◽  
Hydrodynamic Factors

Stratigraphic forward modelling (SFM) is a numerical method that simulates the key factors that control depositional processes. A few models have been developed over years for different geological environment (fluvial, turbidite and carbonate). The model for carbonate system is particularly more complex compared to others. This is due to fact that carbonate system is controlled by the interplay between carbonate productivity, eustasy, subsidence/uplift. Carbonate platform’s morphology also strongly influenced by hydrodynamic factors (Wind and Waves). SFM has been used to test the hypothesis on factors that controlled the evolution of carbonate platforms. This technique also a reliable tool for hydrocarbon exploration and development. SFM has been used to predict carbonate facies distribution, petrophysical properties, and architecture of carbonate platforms. In this review paper, four SFMs namely CARB3D+, GPM CARBONATE, DIONISOS, SEDPAK are discussed.

scholarly journals Porosity Distribution and Differentiation of Different Types of Fluids in Reservoir of Sawan Gas Field, Lower Indus Basin, Pakistan

2019 ◽  
Vol 3 (1) ◽  
pp. 28-37
Author(s):  
M. Asad ◽  
H.U. Rahim
Keyword(s):  
Acoustic Impedance ◽  
Reservoir Characterization ◽  
Three Dimensional ◽  
Seismic Inversion ◽  
Indus Basin ◽  
Gas Field ◽  
Porosity Distribution ◽  
Lower Indus Basin ◽  
Seismic Reservoir ◽  
Lower Goru Formation

AbstractThe lower Indus basin is one of the prolific basins in Pakistan in which the C-interval of lower Goru formation act as a reservoir. With the help of petrophysical interpretation production zone is recognized and also porosity is calculated at the reservoir level. Through porosity we are able to calculate Ksat. A model based inversion of 2D seismic inversion was performed to ascertain three dimensional dispersion of acoustic impedance in the investigation zone and we have recognized new areas where porosity distribution is maximum and site which is suitable for new well. Porosity and Acoustic impedance are typically contrarily relative to each other. Presently porosity can be anticipated in seismic reservoir characterization by utilizing acoustic impedance from seismic inversion far from well position.

scholarly journals Seismic Reservoir Characterization Using Model Based Post-stack Seismic Inversion: In Case of Fenchuganj Gas Field, Bangladesh

2016 ◽  
Vol 59 (6) ◽  
pp. 283-292 ◽  
Author(s):  
Shefa Ul KARIM ◽  
Md Shofiqul ISLAM ◽  
Mohammad Moinul HOSSAIN ◽  
Md Aminul ISLAM
Keyword(s):  
Reservoir Characterization ◽  
Seismic Inversion ◽  
Gas Field ◽  
Model Based ◽  
Seismic Reservoir
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