scholarly journals Artificial Intelligence Aided Geologic Facies Classification in Complex Carbonate Reservoirs

2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Alberto Marsala ◽  
Yanhui Zhang ◽  
Ibrahim Hoteit
Keyword(s):  
Artificial Intelligence ◽  
Reservoir Characterization ◽  
High Efficiency ◽  
Support Vector ◽  
Well Log ◽  
Reservoir Properties ◽  
Model Learning ◽  
Field Development ◽  
Facies Classification ◽  
Classification Framework

Abstract Facies classification for complex reservoirs is an important step in characterizing reservoir heterogeneity and determining reservoir properties and fluid flow patterns. Predicting rock facies automatically and reliably from well log and associated reservoir measurements is therefore essential to obtain accurate reservoir characterization for field development in a timely manner. In this study, we present an artificial intelligence (AI) aided rock facies classification framework for complex reservoirs based on well log measurements. We generalize the AI-aided classification workflow into five major steps including data collection, preprocessing, feature engineering, model learning cycle, and model prediction. In particular, we automate the process of facies classification focusing on the use of a deep learning technique, convolutional neural network, which has shown outstanding performance in many scientific applications involving pattern recognition and classification. For performance analysis, we also compare the developed model with a support vector machine approach. We examine the AI-aided workflow on a large open dataset acquired from a real complex reservoir in Alberta. The dataset contains a collection of well-log measurements over a couple of thousands of wells. The experimental results demonstrate the high efficiency and scalability of the developed framework for automatic facies classification with reasonable accuracy. This is particularly useful when quick facies prediction is necessary to support real-time decision making. The AI-aided framework is easily implementable and expandable to other reservoir applications.

Latest developments in seismic texture analysis for subsurface structure, facies, and reservoir characterization: A review

Geophysics ◽  
2011 ◽  
Vol 76 (2) ◽  
pp. W1-W13 ◽  
Author(s):  
Dengliang Gao
Keyword(s):  
Texture Analysis ◽  
Reservoir Characterization ◽  
Preliminary Test ◽  
Seismic Facies ◽  
Least Squares Regression ◽  
Reservoir Properties ◽  
Seismic Amplitude ◽  
Texture Model ◽  
Facies Classification ◽  
Analysis Window

In exploration geology and geophysics, seismic texture is still a developing concept that has not been sufficiently known, although quite a number of different algorithms have been published in the literature. This paper provides a review of the seismic texture concepts and methodologies, focusing on latest developments in seismic amplitude texture analysis, with particular reference to the gray level co-occurrence matrix (GLCM) and the texture model regression (TMR) methods. The GLCM method evaluates spatial arrangements of amplitude samples within an analysis window using a matrix (a two-dimensional histogram) of amplitude co-occurrence. The matrix is then transformed into a suite of texture attributes, such as homogeneity, contrast, and randomness, which provide the basis for seismic facies classification. The TMR method uses a texture model as reference to discriminate among seismic features based on a linear, least-squares regression analysis between the model and the data within an analysis window. By implementing customized texture model schemes, the TMR algorithm has the flexibility to characterize subsurface geology for different purposes. A texture model with a constant phase is effective at enhancing the visibility of seismic structural fabrics, a texture model with a variable phase is helpful for visualizing seismic facies, and a texture model with variable amplitude, frequency, and size is instrumental in calibrating seismic to reservoir properties. Preliminary test case studies in the very recent past have indicated that the latest developments in seismic texture analysis have added to the existing amplitude interpretation theories and methodologies. These and future developments in seismic texture theory and methodologies will hopefully lead to a better understanding of the geologic implications of the seismic texture concept and to an improved geologic interpretation of reflection seismic amplitude.

scholarly journals Integrated reservoir characterization and fluid flow distribution of the Kaimiro Formation, Taranaki Basin, New Zealand

2020 ◽  
Vol 10 (8) ◽  
pp. 3263-3279 ◽  
Author(s):  
Mohamed Ragab Shalaby ◽  
Syamimi Hana Binti Sapri ◽  
Md Aminul Islam
Keyword(s):  
New Zealand ◽  
Reservoir Characterization ◽  
Flow Distribution ◽  
Log Analysis ◽  
Well Log ◽  
Reservoir Properties ◽  
Flow Units ◽  
Potential Reservoir ◽  
Well Log Analysis ◽  
The Impact

Abstract An integrated reservoir characterization study is achieved on the Early to Middle Miocene Kaimiro Formation in the Taranaki Basin, New Zealand, to identify the quality of the formation as a potential reservoir. The Kaimiro Formation is a section of the Kapuni Group in the Taranaki Basin, consisting mainly of sandstone and a range of coastal plain through shallow marine facies. Several methods were accomplished for this study: petrophysical evaluation, sedimentological and petrographical descriptions and well log analysis. Based on the petrophysical study, the Kaimiro Formation is interpreted to have several flow units ranges up to 15 μm. Higher RQI and FZI reflect potential reservoir, while the pore size and pore throat diameters (r35) are found to be within the range of macro- and megapores, on the contrary to macropores related to poor reservoir quality concentrated in Tui-1 well. This is in good agreement with other measurements that show the formation is exhibited to be a good promising reservoir as the formation comprises a good average porosity of 19.6% and a good average permeability of 879.45 mD. The sedimentological and petrographical studies display that several diagenetic features have been affecting the formation such as compaction, cementation, dissolution and the presence of authigenic clay minerals. Although these features commonly occur, the impact on the reservoir properties and quality is minor as primary and secondary pores are still observed within the Kaimiro sandstone. Moreover, well log analysis is also completed to further ensure the hydrocarbon potential of the formation through a qualitative and quantitative analysis. It has been confirmed that the Kaimiro Formation is a promising reservoir containing several flow units with higher possibility for storage capacity.

scholarly journals Integrated Well Data And 3D Seismic Inversion Study For Reservoir Delineation And Description

2020 ◽  
Vol 70 (1) ◽  
pp. 209-220
Author(s):  
Qazi Sohail Imran ◽  
◽  
Numair Ahmad Siddiqui ◽  
Abdul Halim Abdul Latif ◽  
Yasir Bashir ◽  
...  
Keyword(s):  
Reservoir Characterization ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Depositional Environments ◽  
Well Log ◽  
Reservoir Properties ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
And Performance ◽  
Well Log Analysis

Offshore petroleum systems are often very complex and subtle because of a variety of depositional environments. Characterizing a reservoir based on conventional seismic and well-log stratigraphic analysis in intricate settings often leads to uncertainties. Drilling risks, as well as associated subsurface uncertainties can be minimized by accurate reservoir delineation. Moreover, a forecast can also be made about production and performance of a reservoir. This study is aimed to design a workflow in reservoir characterization by integrating seismic inversion, petrophysics and rock physics tools. Firstly, to define litho facies, rock physics modeling was carried out through well log analysis separately for each facies. Next, the available subsurface information is incorporated in a Bayesian engine which outputs several simulations of elastic reservoir properties, as well as their probabilities that were used for post-inversion analysis. Vast areal coverage of seismic and sparse vertical well log data was integrated by geostatistical inversion to produce acoustic impedance realizations of high-resolution. Porosity models were built later using the 3D impedance model. Lastly, reservoir bodies were identified and cross plot analysis discriminated the lithology and fluid within the bodies successfully.

scholarly journals Hydrocarbon reservoir characterization of “Otan-Ile” field, Niger Delta

2021 ◽  
Vol 11 (2) ◽  
pp. 601-615
Author(s):  
Tokunbo Sanmi Fagbemigun ◽  
Michael Ayu Ayuk ◽  
Olufemi Enitan Oyanameh ◽  
Opeyemi Joshua Akinrinade ◽  
Joel Olayide Amosun ◽  
...  
Keyword(s):  
Niger Delta ◽  
Reservoir Characterization ◽  
Water Saturation ◽  
Structural Deformation ◽  
Well Log ◽  
Reservoir Properties ◽  
Good Tool ◽  
Log Data ◽  
Hydrocarbon Reservoir ◽  
Hydrocarbon Saturation

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.

A new petrophysical joint inversion workflow: Advancing on reservoir’s characterization challenges

2018 ◽  
Vol 6 (3) ◽  
pp. SG33-SG39 ◽  
Author(s):  
Fabio Miotti ◽  
Andrea Zerilli ◽  
Paulo T. L. Menezes ◽  
João L. S. Crepaldi ◽  
Adriano R. Viana
Keyword(s):  
Reservoir Characterization ◽  
Joint Inversion ◽  
Oil Field ◽  
Well Log ◽  
Complementary Information ◽  
Reservoir Properties ◽  
Reservoir Rocks ◽  
Controlled Source ◽  
Fluid Properties

Reservoir characterization objectives are to understand the reservoir rocks and fluids through accurate measurements to help asset teams develop optimal production decisions. Within this framework, we develop a new workflow to perform petrophysical joint inversion (PJI) of seismic and controlled-source electromagnetic (CSEM) data to resolve for reservoirs properties. Our workflow uses the complementary information contained in seismic, CSEM, and well-log data to improve the reservoir’s description drastically. The advent of CSEM, measuring resistivity, brought the possibility of integrating multiphysics data within the characterization workflow, and it has the potential to significantly enhance the accuracy at which reservoir properties and saturation, in particular, can be determined. We determine the power of PJI in the retrieval of reservoir parameters through a case study, based on a deepwater oil field offshore Brazil in the Sergipe-Alagoas Basin, to augment the certainty with which reservoir lithology and fluid properties are constrained.

Simultaneous History-Matching Approach by Use of Reservoir-Characterization and Reservoir-Simulation Studies

2016 ◽  
Vol 19 (04) ◽  
pp. 694-712 ◽  
Author(s):  
Guilherme Daniel Avansi ◽  
Célio Maschio ◽  
Denis José Schiozer
Keyword(s):  
Numerical Simulation ◽  
History Matching ◽  
Reservoir Characterization ◽  
Well Log ◽  
Log Data ◽  
Field Development ◽  
Geostatistical Methods ◽  
Well Production ◽  
Matching Process ◽  
Geological Models

Summary Reservoir characterization is the key to success in history matching and production forecasting. Thus, numerical simulation becomes a powerful tool to achieve a reliable model by quantifying the effect of uncertainties in field development and management planning, calibrating a model with history data, and forecasting field production. History matching is integrated into several areas, such as geology (geological characterization and petrophysical attributes), geophysics (4D-seismic data), statistical approaches (Bayesian theory and Markov field), and computer science (evolutionary algorithms). Although most integrated-history-matching studies use a unique objective function (OF), this is not enough. History matching by simultaneous calibrations of different OFs is necessary because all OFs must be within the acceptance range as well as maintain the consistency of generated geological models during reservoir characterization. The main goal of this work is to integrate history matching and reservoir characterization, applying a simultaneous calibration of different OFs in a history-matching procedure, and keeping the geological consistency in an adjustment approach to reliably forecast production. We also integrate virtual wells and geostatistical methods into the reservoir characterization to ensure realistic geomodels, avoiding the geological discontinuities, to match the reservoir numerical model. The proposed methodology comprises a geostatistical method to model the spatial reservoir-property distribution on the basis of the well-log data; numerical simulation; and adjusting conditional realizations (models) on the basis of geological modeling (variogram model, vertical-proportion curve, and regularized well-log data). In addition, reservoir uncertainties are included, simultaneously adjusting different OFs to evaluate the history-matching process and virtual wells to perturb geological continuities. This methodology effectively preserves the consistency of geological models during the history-matching process. We also simultaneously combine different OFs to calibrate and validate the models with well-production data. Reliable numerical and geological models are used in forecasting production under uncertainties to validate the integrated procedure.

The Laggan and Tormore fields, Blocks 206/1 and 205/5, UK Atlantic Margin

2020 ◽  
Vol 52 (1) ◽  
pp. 967-979 ◽  
Author(s):  
J. Clark ◽  
P. Matthews ◽  
C. Parry ◽  
M. Rowlands ◽  
A. Tessier
Keyword(s):  
Reservoir Characterization ◽  
Reservoir Properties ◽  
Field Development ◽  
Shetland Islands ◽  
Development Scheme ◽  
Seismic Reservoir ◽  
Oil Rim ◽  
The Uk ◽  
Atlantic Margin ◽  
Area Development

AbstractThe Laggan and Tormore fields are found within the Flett sub-basin of the Faroe–Shetland Basin. Situated 120 km west of the Shetland Islands in 600 m water depth, they are part of the deepest subsea development in the UK to date with a 143 km subsea tie-back to onshore facilities.The reservoirs are found within the T35 biostratigraphic sequence of the Paleocene Vaila Formation and comprise sand-rich turbiditic channelized lobes with good reservoir properties, separated by metric to decimetric shale packages. Laggan is a gas-condensate field, whereas Tormore fluid is a richer gas with a saturated oil rim. Seismic reservoir characterization is a key to the field development where differentiation of fluid type proved challenging. Both fields came on stream in 2016 as part of the Greater Laggan area development scheme.

Quantitative log interpretation and uncertainty propagation of petrophysical properties and facies classification from rock-physics modeling and formation evaluation analysis

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. WA45-WA63 ◽  
Author(s):  
Dario Grana ◽  
Marco Pirrone ◽  
Tapan Mukerji
Keyword(s):  
Reservoir Characterization ◽  
Uncertainty Propagation ◽  
Rock Physics ◽  
Integrated Approach ◽  
Physical Models ◽  
Well Log ◽  
Petrophysical Properties ◽  
Formation Evaluation ◽  
Facies Classification ◽  
Well Location

Formation evaluation analysis, rock-physics models, and log-facies classification are powerful tools to link the physical properties measured at wells with petrophysical, elastic, and seismic properties. However, this link can be affected by several sources of uncertainty. We proposed a complete statistical workflow for obtaining petrophysical properties at the well location and the corresponding log-facies classification. This methodology is based on traditional formation evaluation models and cluster analysis techniques, but it introduces a full Monte Carlo approach to account for uncertainty evaluation. The workflow includes rock-physics models in log-facies classification to preserve the link between petrophysical properties, elastic properties, and facies. The use of rock-physics model predictions guarantees obtaining a consistent set of well-log data that can be used both to calibrate the usual physical models used in seismic reservoir characterization and to condition reservoir models. The final output is the set of petrophysical curves with the associated uncertainty, the profile of the facies probabilities, and the entropy, or degree of confusion, related to the most probable facies profile. The full statistical approach allows us to propagate the uncertainty from data measured at the well location to the estimated petrophysical curves and facies profiles. We applied the proposed methodology to two different well-log studies to determine its applicability, the advantages of the new integrated approach, and the value of uncertainty analysis.

Synergistic integration of seismic and geologic data for modeling petrophysical properties

2020 ◽  
Vol 39 (3) ◽  
pp. 164-169
Author(s):  
Yuan Zee Ma ◽  
David Phillips ◽  
Ernest Gomez
Keyword(s):  
Neural Networks ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Integrated Approach ◽  
Reservoir Modeling ◽  
Petrophysical Properties ◽  
Reservoir Properties ◽  
Field Development ◽  
Realistic Description ◽  
Geologic Data

Reservoir characterization and modeling have become increasingly important for optimizing field development. Optimal valuation and exploitation of a field requires a realistic description of the reservoir, which, in turn, requires integrated reservoir characterization and modeling. An integrated approach for reservoir modeling bridges the traditional disciplinary divides and tears down interdisciplinary barriers, leading to better handling of uncertainties and improvement of the reservoir model for field development. This article presents the integration of seismic data using neural networks and the incorporation of a depositional model and seismic data in constructing reservoir models of petrophysical properties. Some challenging issues, including low correlation due to Simpson's paradox and under- or overfitting of neural networks, are mitigated in geostatistical analysis and modeling of reservoir properties by integrating geologic information. This article emphasizes the integration of well logs, seismic prediction, and geologic data in the 3D reservoir-modeling workflow.

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