Carbonate and igneous rock characterization through reprocessing, FWI imaging, and elastic inversion of a legacy seismic data set in Brazilian presalt province

2019 ◽  
Vol 38 (1) ◽  
pp. 11-19 ◽  
Author(s):  
Rodrigo Penna ◽  
Sergio Araújo ◽  
Axel Geisslinger ◽  
Rui Sansonowski ◽  
Leonardo Oliveira ◽  
...  
Keyword(s):  
Flow Behavior ◽  
Igneous Rocks ◽  
Reservoir Modeling ◽  
High Permeability ◽  
Reservoir Properties ◽  
Data Set ◽  
Processing Technologies ◽  
Seismic Processing ◽  
Rock Characterization ◽  
Elastic Inversion

Recent discoveries made in the presalt carbonates of southeast Brazil are among the most important in the past decade. This province, especially Santos Basin, contains large accumulations of oil with excellent quality and high commercial value. The latest seismic processing technologies, along with elastic inversion, have been used for reservoir characterization and in identifying carbonates with high-permeability intervals to model flow behavior of the reservoirs. One challenge encountered is identifying the occurrence of igneous bodies within the reservoir interval and their correct characterization as input into the reservoir modeling. Igneous rocks encountered in the Brazilian presalt can be of both intrusive and extrusive origin. They may present a variety of permo-porous reservoir properties ranging from being flow barriers to acting as high-permeability corridors if fracturing is high enough. The presence of igneous rocks is not common in Brazilian presalt carbonates; therefore, specific methodologies must be used, both in seismic processing and quantitative interpretation, in order to map and predict those occurrences.

Integration of broadband seismic data into reservoir characterization workflows: A case study from the Campos Basin, Brazil

2018 ◽  
Vol 6 (1) ◽  
pp. T145-T161 ◽  
Author(s):  
Ekaterina Kneller ◽  
Manuel Peiro
Keyword(s):  
Reservoir Characterization ◽  
Petroleum Exploration ◽  
Full Potential ◽  
Reservoir Properties ◽  
Data Set ◽  
Low Frequencies ◽  
Campos Basin ◽  
Elastic Inversion

Towed-streamer marine broadband data have been key contributors to recent petroleum exploration history, in new frontiers and in mature basins around the world. They have improved the characterization of reservoirs by reducing the uncertainty in structural and stratigraphic interpretation and by providing more quantitative estimates of reservoir properties. Dedicated acquisition, processing, and quality control (QC) methods have been developed to capitalize on the broad bandwidth of the data and allow their rapid integration into reservoir models. Using a variable-depth steamer data set acquired in the Campos Basin, Brazil, we determine that particular care that should be taken when processing and inverting broadband data to realize their full potential for reservoir interpretation and uncertainty management in the reservoir model. In particular, we determine the QC implemented and interpretative processing approach used to monitor data improvements during processing and preconditioning for elastic inversion. In addition, we evaluate the importance of properly modeling the low frequencies during wavelet estimation. We find the benefits of carefully processed broadband data for structural interpretation and describe the application of acoustic and elastic inversions cascaded with Bayesian lithofacies classification, to provide clear interpretative products with which we were able to demonstrate a reduction in the uncertainty of the prediction and characterization of Santonian oil sandstones in the Campos Basin.

Stochastic inversion of pressure and saturation changes from time-lapse AVO data

Geophysics ◽  
2006 ◽  
Vol 71 (5) ◽  
pp. C81-C92 ◽  
Author(s):  
Helene Hafslund Veire ◽  
Hilde Grude Borgos ◽  
Martin Landrø
Keyword(s):  
Stochastic Model ◽  
Seismic Data ◽  
Synthetic Data ◽  
Time Lapse ◽  
Bayesian Framework ◽  
Reservoir Modeling ◽  
Data Set ◽  
The North ◽  
Fluid Saturation ◽  
Likelihood Model

Effects of pressure and fluid saturation can have the same degree of impact on seismic amplitudes and differential traveltimes in the reservoir interval; thus, they are often inseparable by analysis of a single stacked seismic data set. In such cases, time-lapse AVO analysis offers an opportunity to discriminate between the two effects. We quantify the uncertainty in estimations to utilize information about pressure- and saturation-related changes in reservoir modeling and simulation. One way of analyzing uncertainties is to formulate the problem in a Bayesian framework. Here, the solution of the problem will be represented by a probability density function (PDF), providing estimations of uncertainties as well as direct estimations of the properties. A stochastic model for estimation of pressure and saturation changes from time-lapse seismic AVO data is investigated within a Bayesian framework. Well-known rock physical relationships are used to set up a prior stochastic model. PP reflection coefficient differences are used to establish a likelihood model for linking reservoir variables and time-lapse seismic data. The methodology incorporates correlation between different variables of the model as well as spatial dependencies for each of the variables. In addition, information about possible bottlenecks causing large uncertainties in the estimations can be identified through sensitivity analysis of the system. The method has been tested on 1D synthetic data and on field time-lapse seismic AVO data from the Gullfaks Field in the North Sea.

Fast and accurate detection of surface defect based on improved YOLOv4

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiawei Lian ◽  
Junhong He ◽  
Yun Niu ◽  
Tianze Wang
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Surface Defect ◽  
Steel Ingot ◽  
Industrial Applications ◽  
Data Sets ◽  
Data Set ◽  
Processing Technologies ◽  
Content Type ◽  
Public Data

Purpose The current popular image processing technologies based on convolutional neural network have the characteristics of large computation, high storage cost and low accuracy for tiny defect detection, which is contrary to the high real-time and accuracy, limited computing resources and storage required by industrial applications. Therefore, an improved YOLOv4 named as YOLOv4-Defect is proposed aim to solve the above problems. Design/methodology/approach On the one hand, this study performs multi-dimensional compression processing on the feature extraction network of YOLOv4 to simplify the model and improve the feature extraction ability of the model through knowledge distillation. On the other hand, a prediction scale with more detailed receptive field is added to optimize the model structure, which can improve the detection performance for tiny defects. Findings The effectiveness of the method is verified by public data sets NEU-CLS and DAGM 2007, and the steel ingot data set collected in the actual industrial field. The experimental results demonstrated that the proposed YOLOv4-Defect method can greatly improve the recognition efficiency and accuracy and reduce the size and computation consumption of the model. Originality/value This paper proposed an improved YOLOv4 named as YOLOv4-Defect for the detection of surface defect, which is conducive to application in various industrial scenarios with limited storage and computing resources, and meets the requirements of high real-time and precision.

Artificial Intelligence Coreflooding Simulator for Special Core Data Analysis

2021 ◽  
pp. 1-29
Author(s):  
Eric Sonny Mathew ◽  
Moussa Tembely ◽  
Waleed AlAmeri ◽  
Emad W. Al-Shalabi ◽  
Abdul Ravoof Shaik
Keyword(s):  
Artificial Intelligence ◽  
Steady State ◽  
History Matching ◽  
Water Saturation ◽  
Flow Behavior ◽  
Model Performance ◽  
Analysis Data ◽  
Coefficient Of Determination ◽  
Water Drainage ◽  
Data Set

Two of the most critical properties for multiphase flow in a reservoir are relative permeability (Kr) and capillary pressure (Pc). To determine these parameters, careful interpretation of coreflooding and centrifuge experiments is necessary. In this work, a machine learning (ML) technique was incorporated to assist in the determination of these parameters quickly and synchronously for steady-state drainage coreflooding experiments. A state-of-the-art framework was developed in which a large database of Kr and Pc curves was generated based on existing mathematical models. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from the corefloods including pressure drop and water saturation profile, along with other conventional core analysis data, were fed as features into the ML model. The entire data set was split into 70% for training, 15% for validation, and the remaining 15% for the blind testing of the model. The 70% of the data set for training teaches the model to capture fluid flow behavior inside the core, and then 15% of the data set was used to validate the trained model and to optimize the hyperparameters of the ML algorithm. The remaining 15% of the data set was used for testing the model and assessing the model performance scores. In addition, K-fold split technique was used to split the 15% testing data set to provide an unbiased estimate of the final model performance. The trained/tested model was thereby used to estimate Kr and Pc curves based on available experimental results. The values of the coefficient of determination (R2) were used to assess the accuracy and efficiency of the developed model. The respective crossplots indicate that the model is capable of making accurate predictions with an error percentage of less than 2% on history matching experimental data. This implies that the artificial-intelligence- (AI-) based model is capable of determining Kr and Pc curves. The present work could be an alternative approach to existing methods for interpreting Kr and Pc curves. In addition, the ML model can be adapted to produce results that include multiple options for Kr and Pc curves from which the best solution can be determined using engineering judgment. This is unlike solutions from some of the existing commercial codes, which usually provide only a single solution. The model currently focuses on the prediction of Kr and Pc curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

scholarly journals Petrophysical seismic inversion based on lithofacies classification to enhance reservoir properties estimation: a machine learning approach

2020 ◽  
Author(s):  
Amir Abbas Babasafari ◽  
Shiba Rezaei ◽  
Ahmed Mohamed Ahmed Salim ◽  
Sayed Hesammoddin Kazemeini ◽  
Deva Prasad Ghosh
Keyword(s):  
Acoustic Impedance ◽  
Reservoir Characterization ◽  
Seismic Inversion ◽  
Reservoir Properties ◽  
Well Location ◽  
The Neural Network ◽  
Machine Learning Approach ◽  
Lithofacies Classification ◽  
Elastic Inversion ◽  
Significant Match

Abstract For estimation of petrophysical properties in industry, we are looking for a methodology which results in more accurate outcome and also can be validated by means of some quality control steps. To achieve that, an application of petrophysical seismic inversion for reservoir properties estimation is proposed. The main objective of this approach is to reduce uncertainty in reservoir characterization by incorporating well log and seismic data in an optimal manner. We use nonlinear optimization algorithms in the inversion workflow to estimate reservoir properties away from the wells. The method is applied at well location by fitting nonlinear experimental relations on the petroelastic cross-plot, e.g., porosity versus acoustic impedance for each lithofacies class separately. Once a significant match between the measured and the predicted reservoir property is attained in the inversion workflow, the petrophysical seismic inversion based on lithofacies classification is applied to the inverted elastic property, i.e., acoustic impedance or Vp/Vs ratio derived from seismic elastic inversion to predict the reservoir properties between the wells. Comparison with the neural network method demonstrated this application of petrophysical seismic inversion to be competitive and reliable.

scholarly journals Evaluation of an uncertainty reduction methodology based on Iterative Sensitivity Analysis (ISA) applied to naturally fractured reservoirs

2019 ◽  
Vol 74 ◽  
pp. 40 ◽  
Author(s):  
Luís Augusto Nagasaki Costa ◽  
Célio Maschio ◽  
Denis José Schiozer
Keyword(s):  
Sensitivity Analysis ◽  
History Matching ◽  
Flow Behavior ◽  
Fractured Reservoirs ◽  
Search Space ◽  
Naturally Fractured Reservoirs ◽  
Reservoir Modeling ◽  
Naturally Fractured ◽  
Geostatistical Techniques

History matching for naturally fractured reservoirs is challenging because of the complexity of flow behavior in the fracture-matrix combination. Calibrating these models in a history-matching procedure normally requires integration with geostatistical techniques (Big Loop, where the history matching is integrated to reservoir modeling) for proper model characterization. In problems involving complex reservoir models, it is common to apply techniques such as sensitivity analysis to evaluate and identify most influential attributes to focus the efforts on what most impact the response. Conventional Sensitivity Analysis (CSA), in which a subset of attributes is fixed at a unique value, may over-reduce the search space so that it might not be properly explored. An alternative is an Iterative Sensitivity Analysis (ISA), in which CSA is applied multiple times throughout the iterations. ISA follows three main steps: (a) CSA identifies Group i of influential attributes (i = 1, 2, 3, …, n); (b) reduce uncertainty of Group i, with other attributes with fixed values; and (c) return to step (a) and repeat the process. Conducting CSA multiple times allows the identification of influential attributes hidden by the high uncertainty of the most influential attributes. In this work, we assess three methods: Method 1 – ISA, Method 2 – CSA, and Method 3 – without sensitivity analysis, i.e., varying all uncertain attributes (larger searching space). Results showed that the number of simulation runs for Method 1 dropped 24% compared to Method 3 and 12% to Method 2 to reach a similar matching quality of acceptable models. In other words, Method 1 reached a similar quality of results with fewer simulations. Therefore, ISA can perform as good as CSA demanding fewer simulations. All three methods identified the same five most influential attributes of the initial 18. Even with many uncertain attributes, only a small percentage is responsible for most of the variability of responses. Also, their identification is essential for efficient history matching. For the case presented in this work, few fracture attributes were responsible for most of the variability of the responses.

High-Strain-Rate Superplastic Flow Mechanism in ZrO2-30vol% Spinel Two-Phase Composite

2010 ◽  
Vol 433 ◽  
pp. 333-338 ◽  
Author(s):  
Koji Morita ◽  
Keijiro Hiraga ◽  
Byung Nam Kim ◽  
Hidehiro Yoshida
Keyword(s):  
High Strain Rate ◽  
Strain Rate ◽  
High Strain ◽  
Flow Behavior ◽  
Recovery Process ◽  
Lattice Diffusion ◽  
Two Phase ◽  
Data Set ◽  
Strain And Strain Rate ◽  
Accommodation Process

High-strain-rate superplasticity (HSRS) can be attained in tetragonal ZrO2-30vol% MgAl2O4 spinel composite. In order to examine the flow behavior of the two-phase composite, the standard rule of the mixture model was employed. The strain rate of the composite can be explained by the isostrain model that is predicted from the data set of Al2O3 doped ZrO2 and spinel polycrystals. For the isostrain model, since the strain and strain rate are the same for ZrO2 and spinel phases, the harder ZrO2 phase carries more of the stress in the composite. In order to preserve homogeneous deformation and material continuity, a concomitant accommodation process within the harder ZrO2 grains is also necessary. For HSRS in the ZrO2-spinel composite, therefore, the rate of deformation may be controlled by the slower dislocation recovery process limited by the lattice diffusion within harder ZrO2 grains rather than within spinel grains.

Optimal dynamic rock-fluid physics template validated by petroelastic reservoir modeling

Geophysics ◽  
2011 ◽  
Vol 76 (6) ◽  
pp. O45-O58 ◽  
Author(s):  
Alireza Shahin ◽  
Robert Tatham ◽  
Paul Stoffa ◽  
Kyle Spikes
Keyword(s):  
Sensitivity Analysis ◽  
Acoustic Impedance ◽  
Velocity Ratio ◽  
Rapid Change ◽  
Time Lapse ◽  
Seismic Attributes ◽  
Reservoir Modeling ◽  
Reservoir Model ◽  
Reservoir Properties ◽  
Fluid Saturation

Separation of fluid pore pressure and saturation using inverted time-lapse seismic attributes is a mandatory task for field development. Multiple pairs of inversion-derived attributes can be used in a crossplot domain. We performed a sensitivity analysis to determine an optimal crossplot, and the validity of the separation is tested with a comprehensive petroelastic reservoir model. We simulated a poorly consolidated shaly sandstone reservoir based on a prograding near-shore depositional environment. A model of effective porosity is first simulated by Gaussian geostatistics. Well-known theoretical and experimental petrophysical correlations were then efficiently combined to consistently simulate reservoir properties. Next, the reservoir model was subjected to numerical simulation of multiphase fluid flow to predict the spatial distributions of fluid saturation and pressure. A geologically consistent rock physics model was then used to simulate the inverted seismic attributes. Finally, we conducted a sensitivity analysis of seismic attributes and their crossplots as a tool to discriminate the effect of pressure and saturation. The sensitivity analysis demonstrates that crossplotting of acoustic impedance versus shear impedance should be the most stable way to separate saturation and pressure changes compared to other crossplots (e.g., velocity ratio versus acoustic impedance). We also demonstrated that the saturation and pressure patterns were detected in most of the time-lapse scenarios; however, the saturation pattern is more likely detectable because the percentage in pressure change is often lower than that of the saturation change. Imperfections in saturation and pressure patterns exist in various forms, and they can be explained by the interaction of saturation and pressure, the diffusive nature of pressure, and rapid change in pressure due to production operations.

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