Lateral prediction of reservoir properties using seismic attributes with well log data

AbstractOtan-Ile field, located in the transition zone Niger Delta, is characterized by complex structural deformation and faulting which lead to high uncertainties of reservoir properties. These high uncertainties greatly affect the exploration and development of the Otan-Ile field, and thus require proper characterization. Reservoir characterization requires integration of different data such as seismic and well log data, which are used to develop proper reservoir model. Therefore, the objective of this study is to characterize the reservoir sand bodies across the Otan-Ile field and to evaluate the petrophysical parameters using 3-dimension seismic and well log data from four wells. Reservoir sands were delineated using combination of resistivity and gamma ray logs. The estimation of reservoir properties, such as gross thickness, net thickness, volume of shale, porosity, water saturation and hydrocarbon saturation, were done using standard equations. Two horizons (T and U) as well as major and minor faults were mapped across the ‘Otan-Ile’ field. The results show that the average net thickness, volume of shale, porosity, hydrocarbon saturation and permeability across the field are 28.19 m, 15%, 37%, 71% and 26,740.24 md respectively. Two major faults (F1 and F5) dipping in northeastern and northwestern direction were identified. The horizons were characterized by structural closures which can accommodate hydrocarbon were identified. Amplitude maps superimposed on depth-structure map also validate the hydrocarbon potential of the closures on it. This study shows that the integration of 3D seismic and well log data with seismic attribute is a good tool for proper hydrocarbon reservoir characterization.

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Geophysical imaging of porosity variations in the Danish North Sea chalk

Geological Survey of Denmark and Greenland Bulletin ◽

10.34194/geusb.v15.5033 ◽

2008 ◽

Vol 15 ◽

pp. 17-20 ◽

Cited By ~ 1

Author(s):

Tanni Abramovitz

Keyword(s):

North Sea ◽

Seismic Reflection ◽

Seismic Inversion ◽

Rock Properties ◽

Well Log ◽

Reservoir Properties ◽

Seismic Reflection Data ◽

Log Data ◽

The North ◽

The North Sea

More than 80% of the present-day oil and gas production in the Danish part of the North Sea is extracted from fields with chalk reservoirs of late Cretaceous (Maastrichtian) and early Paleocene (Danian) ages (Fig. 1). Seismic reflection and in- version data play a fundamental role in mapping and characterisation of intra-chalk structures and reservoir properties of the Chalk Group in the North Sea. The aim of seismic inversion is to transform seismic reflection data into quantitative rock properties such as acoustic impedance (AI) that provides information on reservoir properties enabling identification of porosity anomalies that may constitute potential reservoir compartments. Petrophysical analyses of well log data have shown a relationship between AI and porosity. Hence, AI variations can be transformed into porosity variations and used to support detailed interpretations of porous chalk units of possible reservoir quality. This paper presents an example of how the chalk team at the Geological Survey of Denmark and Greenland (GEUS) integrates geological, geophysical and petrophysical information, such as core data, well log data, seismic 3-D reflection and AI data, when assessing the hydrocarbon prospectivity of chalk fields.

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Estimation of Reservoir Properties from Well Log Data Using Artificial Neural Networks

71st EAGE Conference and Exhibition incorporating SPE EUROPEC 2009 ◽

10.3997/2214-4609.201401402 ◽

2009 ◽

Author(s):

M. Sitouah

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Well Log ◽

Reservoir Properties ◽

Log Data ◽

Artificial Neural

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A new approach to improve reservoir modeling via machine learning

The Leading Edge ◽

10.1190/tle39030170.1 ◽

2020 ◽

Vol 39 (3) ◽

pp. 170-175

Author(s):

Islam A. Mohamed ◽

Adel Othman ◽

Mohamed Fathy

Keyword(s):

Nile Delta ◽

Modeling Method ◽

Reservoir Modeling ◽

Early Model ◽

Well Log ◽

Well Test ◽

Reservoir Properties ◽

New Approach ◽

Log Data ◽

Simulation Process

In highly heterogeneous basins with complex subsurface geology, such as the Nile Delta Basin, accurate prediction of reservoir modeling has been a challenge. Reservoir modeling is a continuous process that begins with field discovery and ends with the last phases of production and abandonment. Currently, the stochastic reservoir modeling method is widely used instead of the traditional deterministic modeling method to consider spatial statistics and uncertainties. However, the modeling workflow is demanding and slow, typically requiring months from the initial model concept to flow simulation. In addition, errors from early model stages become cumulative and are difficult to change retroactively. To overcome these limitations, a new workflow is proposed that implements probabilistic neural network inversion to predict reservoir properties. First, well-log data were conditioned properly to match the seismic data scale. Then, the networks were trained and validated, using the conditioned well-log data and seismic internal/external attributes, to predict water saturation and effective porosity 3D volumes. The resulting volumes were sampled in simulation 3D grids and tested using a blind well test. Subsequently, the permeability was calculated from a porosity-permeability relationship inside the reservoir. Finally, a dynamic simulation project of the field was performed in which the historical field production and pressures were compared to the predicted values. One of the Pliocene deepwater turbidite reservoirs in the offshore Nile Delta was used to demonstrate the proposed approach. The results proved the accuracy of the model in predicting the reservoir properties and honoring the heterogeneity of the reservoir. The new approach represents a shortcut for the seismic-to-simulation process, providing a reliable and fast way of constructing a reservoir model and making the seismic-to-simulation process easier.

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Porosity Prediction of a Carbonate Reservoir in Campos Basin Based on the Integration of Seismic Attributes and Well Log Data

Oil and Gas Wells ◽

10.5772/intechopen.82490 ◽

2020 ◽

Author(s):

Roberta Tomi Mori ◽

Emilson Pereira Leite

Keyword(s):

Seismic Attributes ◽

Carbonate Reservoir ◽

Well Log ◽

Log Data ◽

Campos Basin

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Petrophysical seismic inversion conditioned to well-log data: Methods and application to a gas reservoir

Geophysics ◽

10.1190/1.3043796 ◽

2009 ◽

Vol 74 (2) ◽

pp. O1-O15 ◽

Cited By ~ 56

Author(s):

Miguel Bosch ◽

Carla Carvajal ◽

Juan Rodrigues ◽

Astrid Torres ◽

Milagrosa Aldana ◽

...

Keyword(s):

Multiscale Model ◽

Problem Formulation ◽

Monte Carlo Sampling ◽

Seismic Inversion ◽

Well Log ◽

Random Component ◽

Reservoir Properties ◽

Gas Reservoir ◽

Statistical Characterization ◽

Log Data

Hydrocarbon reservoirs are characterized by seismic, well-log, and petrophysical information, which is dissimilar in spatial distribution, scale, and relationship to reservoir properties. We combine this diverse information in a unified inverse-problem formulation using a multiproperty, multiscale model, linking properties statistically by petrophysical relationships and conditioning them to well-log data. Two approaches help us: (1) Markov-chain Monte Carlo sampling, which generates many reservoir realizations for estimating medium properties and posterior marginal probabilities, and (2) optimization with a least-squares iterative technique to obtain the most probable model configuration. Our petrophysical model, applied to near-vertical-anglestackedseismic data and well-log data from a gas reservoir, includes a deterministic component, based on a combination of Wyllie and Wood relationships calibrated with the well-log data, and a random component, based on the statistical characterization of the deviations of well-log data from the petrophysical transform. At the petrophysical level, the effects of porosity and saturation on acoustic impedance are coupled; conditioning the inversion to well-log data helps resolve this ambiguity. The combination of well logs, petrophysics, and seismic inversion builds on the corresponding strengths of each type of information, jointly improving (1) cross resolution of reservoir properties, (2) vertical resolution of property fields, (3) compliance to the smooth trend of property fields, and (4) agreement with well-log data at well positions.

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