Seismic Driven Reservoir Characterization: Case Studies of Reservoir Architecture Definition and Reservoir Rock Properties Distribution

2011 ◽  
Author(s):  
G. Gonzales ◽  
Y. Boisseau
Keyword(s):  
Case Studies ◽  
Reservoir Characterization ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Reservoir Architecture

scholarly journals Digital core repository coupled with machine learning as a tool to classify and assess petrophysical rock properties

2020 ◽  
Vol 146 ◽  
pp. 01003
Author(s):  
Vanessa Hébert ◽  
Thierry Porcher ◽  
Valentin Planes ◽  
Marie Léger ◽  
Anna Alperovich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Network Modelling ◽  
3D Images ◽  
Digital Core ◽  
Benchmark Datasets

To make efficient use of image-based rock physics workflow, it is necessary to optimize different criteria, among which: quantity, representativeness, size and resolution. Advances in artificial intelligence give insights of databases potential. Deep learning methods not only enable to classify rock images, but could also help to estimate their petrophysical properties. In this study we prepare a set of thousands high-resolution 3D images captured in a set of four reservoir rock samples as a base for learning and training. The Voxilon software computes numerical petrophysical analysis. We identify different descriptors directly from 3D images used as inputs. We use convolutional neural network modelling with supervised training using TensorFlow framework. Using approximately fifteen thousand 2D images to drive the classification network, the test on thousand unseen images shows any error of rock type misclassification. The porosity trend provides good fit between digital benchmark datasets and machine learning tests. In a few minutes, database screening classifies carbonates and sandstones images and associates the porosity values and distribution. This work aims at conveying the potential of deep learning method in reservoir characterization to petroleum research, to illustrate how a smart image-based rock physics database at industrial scale can swiftly give access to rock properties.

Integrating Rock Typing Methods Including Empirical, Deterministic, Statistical, Probabilistic, Predictive Techniques and New Applications for Practical Reservoir Characterization

2021 ◽  
Author(s):  
Gary William William Gunter ◽  
Mohamed Yacine Yacine Sahar ◽  
David F. Allen ◽  
Eduardo Jose Viro ◽  
Shahin Negabahn ◽  
...  
Keyword(s):  
Case Studies ◽  
Reservoir Characterization ◽  
Rock Properties ◽  
Rock Typing ◽  
Open Hole ◽  
Infill Drilling ◽  
Single Well ◽  
Predictive Approaches ◽  
3D Volume ◽  
New Applications

Abstract This paper discusses integrating common methods and applications for "Rock Typing" (also known as Petrophysical Rock Typing-PRT) including empirical, deterministic, statistical, probalistic and automatic/predictive approaches. Many industry asset teams apply one or more of these methods when creating static reservoir models, using dynamic reservoir simulations, completing petrophysical studies for saturation height models and determining reservoir volumetrics as part of reservoir characterization studies. Our intention is to provide guidance and important information on how and when to use the various methods, so people can make an informed selection. This discussion is important as many disciplines apply these PRT techniques without understanding the pros, cons and limitations of the different methods. An important tool is comparing PRT results from multiple methods. The topics and workflows that are covered focus on various PRT techniques and workflows. We will use case-studies to illustrate the key features and make important comparisons. Key results include comparing pros and cons, how to use and combine multiple PRT techniques and verify results. This paper includes these techniques and workflows;MICP, core analysis and pore throat calibration.Core-Log Integration focused on PRT analysis.Winland, Pittman, Aguilera and Hartmann et.al Gameboard methods.K-Phi ratio, Flow Zone Indicators and Rock Quality Index methods.Classic, Modified and Stratigraphic Lorenz methods.IPSOM and HRA Probabilistic methods.Case Study – Super Plot and Advanced Automatic PRT Method.Special Topics – Carbonate Methods, NMR and Single Well Vertical Line. Practical approaches based on case studies show how PRT analysis can be applied in mature fields to identify by-passed hydrocarbon zones and zones that have a high probability of producing water using open hole, cased hole and production logs. Traditional Rock Typing (PRT) analysis can be applied as a single well technique or as a multi-well method so operations teams can identify additional business opportunities (remedial workovers, infill drilling locations or exploitation targets) and compare reservoir performance with intrinsic rock properties. New applications and additional topics cover single, multiple well approaches and new emerging PRT techniques (including NMR well logs and machine learning). We recommend how to merge classic facies with PRT analysis for 3-D applications including populating a 3D volume.

scholarly journals Estimation of reservoir properties from seismic data by smooth neural networks

Geophysics ◽  
2003 ◽  
Vol 68 (6) ◽  
pp. 1969-1983 ◽  
Author(s):  
M. M. Saggaf ◽  
M. Nafi Toksöz ◽  
H. M. Mustafa
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Back Propagation ◽  
Reservoir Rock ◽  
Seismic Survey ◽  
Rock Properties ◽  
Reservoir Properties ◽  
Network Parameters ◽  
Generalization Problem ◽  
Validation Tests

The performance of traditional back‐propagation networks for reservoir characterization in production settings has been inconsistent due to their nonmonotonous generalization, which necessitates extensive tweaking of their parameters in order to achieve satisfactory results and avoid overfitting the data. This makes the accuracy of these networks sensitive to the selection of the network parameters. We present an approach to estimate the reservoir rock properties from seismic data through the use of regularized back propagation networks that have inherent smoothness characteristics. This approach alleviates the nonmonotonous generalization problem associated with traditional networks and helps to avoid overfitting the data. We apply the approach to a 3D seismic survey in the Shedgum area of Ghawar field, Saudi Arabia, to estimate the reservoir porosity distribution of the Arab‐D zone, and we contrast the accuracy of our approach with that of traditional back‐propagation networks through cross‐validation tests. The results of these tests indicate that the accuracy of our approach remains consistent as the network parameters are varied, whereas that of the traditional network deteriorates as soon as deviations from the optimal parameters occur. The approach we present thus leads to more robust estimates of the reservoir properties and requires little or no tweaking of the network parameters to achieve optimal results.

P- and S-wave attenuation logs from monopole sonic data

Geophysics ◽  
2000 ◽  
Vol 65 (3) ◽  
pp. 755-765 ◽  
Author(s):  
Xinhua Sun ◽  
Xiaoming Tang ◽  
C. H. (Arthur) Cheng ◽  
L. Neil Frazer
Keyword(s):  
Wave Attenuation ◽  
Fluid Velocity ◽  
P Wave ◽  
Reservoir Rock ◽  
Rock Properties ◽  
S Wave ◽  
Intrinsic Attenuation ◽  
Modified Method ◽  
Receiver Array ◽  
Attenuation Profiles

In this paper, a modification of an existing method for estimating relative P-wave attenuation is proposed. By generating synthetic waveforms without attenuation, the variation of geometrical spreading related to changes in formation properties with depth can be accounted for. With the modified method, reliable P- and S-wave attenuation logs can be extracted from monopole array acoustic waveform log data. Synthetic tests show that the P- and S-wave attenuation values estimated from synthetic waveforms agree well with their respective model values. In‐situ P- and S-wave attenuation profiles provide valuable information about reservoir rock properties. Field data processing results show that this method gives robust estimates of intrinsic attenuation. The attenuation profiles calculated independently from each waveform of an eight‐receiver array are consistent with one another. In fast formations where S-wave velocity exceeds the borehole fluid velocity, both P-wave attenuation ([Formula: see text]) and S-wave attenuation ([Formula: see text]) profiles can be obtained. P- and S-wave attenuation profiles and their comparisons are presented for three reservoirs. Their correlations with formation lithology, permeability, and fractures are also presented.

Comprehensive study of the geological structure Verkhneyakovlev deposits of the Tagul field in the process of justifications for operational drilling

2021 ◽  
pp. 85-97
Author(s):  
A. S. Titenkov ◽  
Yu. N. Utyashev ◽  
A. A. Evdoshchuk ◽  
V. A. Belkina ◽  
D. V. Grandov
Keyword(s):  
Complex Structure ◽  
Geological Structure ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Capital Costs ◽  
Complex Geological Structure ◽  
Well Correlation ◽  
Effective Development ◽  
The Cost ◽  
Available Information

Currently, most of the fields being put into development are characterized by a complex geological structure, both in terms of section and in terms of plan. The solution of all geological tasks, including such important ones as the preparation of exploration projects, operation and effective development management, is impossible without creating models that reflect the main features of the variability of target parameters. The construction of adequate models of objects with a complex structure requires the involvement of all available information. The accuracy of the geological model is mostly determined by the accuracy of the well correlation. Paleosols are a new marker for the complex-built layers of the VAk-2 and VAk-3(1) of the Tagul field, which contributes to the validity of the correlation of the section of these layers. The reliability of the model was also improved by the use of the results of facies analysis. This analysis showed that the sedimentation of the studied objects includes channel and floodplain facies. Reservoir rock properties of these facies differ significantly. The updated model is characterized by a reduction in the oil-bearing area and the amount of reserves. The implementation of the model will optimize the project fund of wells and reduce the cost of well intervention. Economically, this means reducing capital costs and increasing the profitability of the project.

scholarly journals Reservoir Performance Prediction using Integrated Production Modelling (MBAL Software)

2019 ◽  
Vol 8 (4) ◽  
pp. 1484-1489
Keyword(s):  
Performance Prediction ◽  
History Matching ◽  
Material Balance ◽  
Development Planning ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Production Operation ◽  
Gas Field ◽  
Reservoir Performance ◽  
Fluid Properties

Reservoir performance prediction is important aspect of the oil & gas field development planning and reserves estimation which depicts the behavior of the reservoir in the future. Reservoir production success is dependent on precise illustration of reservoir rock properties, reservoir fluid properties, rock-fluid properties and reservoir flow performance. Petroleum engineers must have sound knowledge of the reservoir attributes, production operation optimization and more significant, to develop an analytical model that will adequately describe the physical processes which take place in the reservoir. Reservoir performance prediction based on material balance equation which is described by Several Authors such as Muskat, Craft and Hawkins, Tarner’s, Havlena & odeh, Tracy’s and Schilthuis. This paper compares estimation of reserve using dynamic simulation in MBAL software and predictive material balance method after history matching of both of this model. Results from this paper shows functionality of MBAL in terms of history matching and performance prediction. This paper objective is to set up the basic reservoir model, various models and algorithms for each technique are presented and validated with the case studies. Field data collected related to PVT analysis, Production and well data for quality check based on determining inconsistencies between data and physical reality with the help of correlations. Further this paper shows history matching to match original oil in place and aquifer size. In the end conclusion obtained from different plots between various parameters reflect the result in history match data, simulation result and Future performance of the reservoir system and observation of these results represent similar simulation and future prediction plots result.

COMPARISON OF FRACTIONAL NOISE DISTRIBUTIONS FOR GENERATING HETEROGENEOUS PERMEABILITY FIELDS IN OIL RESERVOIR SIMULATIONS: A CASE STUDY

Fractals ◽  
2018 ◽  
Vol 26 (04) ◽  
pp. 1850066
Author(s):  
MARYAM GHORBANI ◽  
MOHAMMAD REZA KHORSAND MOVAGHAR
Keyword(s):  
Hurst Exponent ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Heterogeneous Reservoirs ◽  
Regionalized Variables ◽  
Geostatistical Models ◽  
Permeability Distribution ◽  
Simulation Results ◽  
Core Permeability ◽  
Precise Simulation

Prediction of reservoir rock properties, especially permeability distribution is needed for precise simulation of heterogeneous reservoirs. Interwell permeability fields have recently been considered for dynamic simulation using geostatistical models and fractal geometries. The geostatistical models employ experimentally observed variograms to characterize the spatial variability of regionalized variables such as permeability. Fractal models can be useful in assessing the spatial correlation of a property because their variogram can be characterized with a single parameter called the Hurst exponent. In this study, based on core permeability data of each well, Hurst exponent (using [Formula: see text] analysis) is assigned locally to each well by means of stream lines and as averaged value for interwell spaces. Then, permeability distributions are created using Fractional Brownian Motion (FBM) and Fractional Gaussian Noise (FGN) models by implementing fast Fourier transform (FFT). Through comparison between simulation results of these models, as well as real grid simulation results, the averaged distribution was shown to give better results over a locally assigned fractal distribution. Furthermore, predictions of field pressure using the FGN model were shown to function better than the FBM model for vertical wells.

3D PETROPHYSICS FOR HAWE: CASE STUDIES

2021 ◽  
Author(s):  
Alexander Kolomytsev ◽  
◽  
Yulia Pronyaeva Pronyaeva ◽  
Keyword(s):  
Case Studies ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Vertical Direction ◽  
Geological Structure ◽  
Current Layer ◽  
Radial Model ◽  
The Difference ◽  
Geological Concept ◽  
Property Changes

Most conventional log interpretation technics use the radial model, which was developed for vertical wells and work well in them. But applying this model to horizontal wells can result in false conclusions. The reasons for this are property changes in vertical direction and different depth of investigation (DOI) of logging tools. DOI area probably can include a response from different layers with different properties. All of this complicates petrophysical modeling. The 3D approach for high angle well evaluation (HAWE) is forward modeling in 3D. For this modeling, it is necessary to identify the geological concept near the horizontal well section using multiscale data. The accuracy of modeling depends on the details of the accepted geological model based on the data of borehole images, logs, geosteering inversion, and seismic data. 3D modeling can be applied to improve the accuracy of reservoir characterization, well placement, and completion. The radial model is often useless for HAWE because LWD tools have different DOI and the invasion zone was not formed. But the difference between volumetric and azimuthal measurements is important for comprehensive interpretation because various formations have different properties in vertical directions. Resistivity tools have the biggest DOI. It is important to understand and be able to determine the reason for changes in log response: a change in the properties of the current layer or approaching the layers with other properties. For this, it is necessary to know the distance to the boundaries of formations with various properties and, therefore, to understand the geological structure of the discovered deposits, and such information on the scale of well logs can be obtained either by modeling or by using extra deep resistivity inversion (mapping). The largest amount of multidisciplinary information is needed for modeling purposes - from images and logs to mapping and seismic data. Case studies include successful examples from Western Siberia clastic formations. In frame of the cases, different tasks have been solved: developed geological concept, updated petrophysical properties for STOIIP and completion, and provided solutions during geosteering. Multiscale modeling, which includes seismic, geosteering mapping data, LWD, and imagers, has been used for all cases.

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