Bayesian facies inversion on a partially dolomitized isolated carbonate platform. A case study from Central Luconia province, Malaysia.

Geophysics ◽  
2020 ◽  
pp. 1-47
Author(s):  
George Ghon ◽  
Dario Grana ◽  
Eugene C. Rankey ◽  
Gregor T. Baechle ◽  
Florian Bleibinhaus ◽  
...  
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Carbonate Platform ◽  
Gaussian Mixture ◽  
Model Parameters ◽  
Elastic Model ◽  
Probabilistic Prediction ◽  
S Wave ◽  
Porosity Distribution

We present a case study of geophysical reservoir characterization where we use elastic inversion and probabilistic prediction to predict 9 carbonate lithofacies and the associated porosity distribution. The study focuses on an isolated carbonate platform of middle Miocene age, offshore Sarawak in Malaysia, which has been partly dolomitized — a process that increased porosity and permeability of the prolific gas reservoir. The 9 lithofacies are defined from one reference core and include a range of lithologies and pore types, covering limestone and dolomitized limestone, each with vuggy varieties, as well as sucrosic and crystalline dolomites with intercrystalline porosity, and also argillaceous limestones, and shales. To predict lithofacies and porosity from geophysical data, we adopt a probabilistic algorithm that employs Bayesian theory with an analytical solution for conditional means and covariances of posterior probabilities, assuming a Gaussian mixture model. The inversion is a 2-step process, first solving for elastic model parameters P- and S-wave velocities and density from 2 partial seismic stacks. Subsequently, lithofacies and porosity are predicted from the elastic parameters in the borehole and across a 2-D inline. The final result is a model that consists of the pointwise posterior distributions of facies and porosity at each location where seismic data are available. The facies posterior distribution represents the facies proportions estimated from seismic data, whereas the porosity distribution represents the the probability density function at each location. These distributions provide the most likely model and its associated uncertainty for geological interpretations.

Applications of Artificial Neural Networks for Seismic Facies Classification

2021 ◽  
Vol 73 (02) ◽  
pp. 68-69
Author(s):  
Chris Carpenter
Keyword(s):  
Neural Network ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Probabilistic Neural Network ◽  
Oil Field ◽  
Seismic Facies ◽  
Data Set ◽  
Full Field ◽  
Facies Classification

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 200577, “Applications of Artificial Neural Networks for Seismic Facies Classification: A Case Study From the Mid-Cretaceous Reservoir in a Supergiant Oil Field,” by Ali Al-Ali, Karl Stephen, SPE, and Asghar Shams, Heriot-Watt University, prepared for the 2020 SPE Europec featured at the 82nd EAGE Conference and Exhibition, originally scheduled to be held in Amsterdam, 1-3 December. The paper has not been peer reviewed. Facies classification using data from sources such as wells and outcrops cannot capture all reservoir characterization in the interwell region. Therefore, as an alternative approach, seismic facies classification schemes are applied to reduce the uncertainties in the reservoir model. In this study, a machine-learning neural network was introduced to predict the lithology required for building a full-field Earth model for carbonate reservoirs in southern Iraq. The work and the methodology provide a significant improvement in facies classification and reveal the capability of a probabilistic neural network technique. Introduction The use of machine learning in seismic facies classification has increased gradually during the past decade in the interpretation of 3D and 4D seismic volumes and reservoir characterization work flows. The complete paper provides a literature review regarding this topic. Previously, seismic reservoir characterization has revealed the heterogeneity of the Mishrif reservoir and its distribution in terms of the pore system and the structural model. However, the main objective of this work is to classify and predict the heterogeneous facies of the carbonate Mishrif reservoir in a giant oil field using a multilayer feed-forward network (MLFN) and a probabilistic neural network (PNN) in nonlinear facies classification techniques. A related objective was to find any domain-specific causal relationships among input and output variables. These two methods have been applied to classify and predict the presence of different facies in Mishrif reservoir rock types. Case Study Reservoir and Data Set Description. The West Qurna field is a giant, multibillion-barrel oil field in the southern Mesopotamian Basin with multiple carbonate and clastic reservoirs. The overall structure of the field is a north/south trending anticline steep on the western flank and gentle on the eastern flank. Many producing reservoirs developed in this oil field; however, the Mid- Cretaceous Mishrif reservoir is the main producing reservoir. The reservoir consists of thick carbonate strata (roughly 250 m) deposited on a shallow water platform adjacent to more-distal, deeper-water nonreservoir carbonate facies developing into three stratigraphic sequence units in the second order. Mishrif facies are characterized by a porosity greater than 20% and large permeability contrast from grainstones to microporosity (10-1000 md). The first full-field 3D seismic data set was achieved over 500 km2 during 2012 and 2013 in order to plan the development of all field reservoirs. A de-tailed description of the reservoir has been determined from well logs and core and seismic data. This study is mainly based on facies log (22 wells) and high-resolution 3D seismic volume to generate seismic attributes as the input data for the training of the neural network model. The model is used to evaluate lithofacies in wells without core data but with appropriate facies logs. Also, testing was carried out in parallel with the core data to verify the results of facies classification.

Reservoir characterization over the Lille Prinsen and Ivar Aasen fields in the Norwegian North Sea using ocean-bottom-node seismic data — A case study

2021 ◽  
pp. 1-64
Author(s):  
Satinder Chopra ◽  
Ritesh Kumar Sharma ◽  
Mikal Trulsvik ◽  
Adriana Citlali Ramirez ◽  
David Went ◽  
...  
Keyword(s):  
North Sea ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Water Saturation ◽  
Late Triassic ◽  
Ocean Bottom ◽  
Impedance Inversion ◽  
High Area ◽  
Elastic Impedance

An integrated workflow is proposed for estimating elastic parameters within the Late Triassic Skagerrak Formation, the Middle Jurassic Sleipner and Hugin Formations, the Paleocene Heimdal Formation and Eocene Grid Formation in the Utsira High area of the Norwegian North Sea. The proposed workflow begins with petrophysical analysis carried out at the available wells. Next, model-based prestack simultaneous impedance inversion outputs were derived, and attempts were made to estimate the petrophysical parameters (volume of shale, porosity, and water saturation) from seismic data using extended elastic impedance. On not obtaining convincing results, we switched over to multiattribute regression analysis for estimating them, which yielded encouraging results. Finally, the Bayesian classification approach was employed for defining different facies in the intervals of interest.

Comparison of P‐ and S‐wave seismic data: A new method for detecting gas reservoirs

Geophysics ◽  
1984 ◽  
Vol 49 (9) ◽  
pp. 1420-1431 ◽  
Author(s):  
Ross Alan Ensley
Keyword(s):  
Shear Wave ◽  
Seismic Data ◽  
Quantitative Methods ◽  
Qualitative Evaluation ◽  
Compressional Wave ◽  
Bright Spot ◽  
S Wave ◽  
Gas Reservoirs ◽  
Compressional Waves

Compressional waves are sensitive to the type of pore fluid within rocks, but shear waves are only slightly affected by changes in fluid type. This suggests that a comparison of compressional‐ and shear‐wave seismic data recorded over a prospect may allow an interpreter to discriminate between gas‐related anomalies and those related to lithology. This case study documents that where a compressional‐wave “bright spot” or other direct hydrocarbon indicator is present, such a comparison can be used to verify the presence of gas. In practice, the technique can only be used for a qualitative evaluation. However, future improvement of shear‐wave data quality may enable the use of more quantitative methods as well.

Simultaneous inversion of PP and PS seismic data

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. R1-R10 ◽  
Author(s):  
Helene Hafslund Veire ◽  
Martin Landrø
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Model Building ◽  
Joint Inversion ◽  
P Wave ◽  
Inversion Method ◽  
System Of Equations ◽  
S Wave ◽  
Data Set ◽  
Simultaneous Inversion

Elastic parameters derived from seismic data are valuable input for reservoir characterization because they can be related to lithology and fluid content of the reservoir through empirical relationships. The relationship between physical properties of rocks and fluids and P-wave seismic data is nonunique. This leads to large uncertainties in reservoir models derived from P-wave seismic data. Because S- waves do not propagate through fluids, the combined use of P-and S-wave seismic data might increase our ability to derive fluid and lithology effects from seismic data, reducing the uncertainty in reservoir characterization and thereby improving 3D reservoir model-building. We present a joint inversion method for PP and PS seismic data by solving approximated linear expressions of PP and PS reflection coefficients simultaneously using a least-squares estimation algorithm. The resulting system of equations is solved by singular-value decomposition (SVD). By combining the two independent measurements (PP and PS seismic data), we stabilize the system of equations for PP and PS seismic data separately, leading to more robust parameter estimation. The method does not require any knowledge of PP and PS wavelets. We tested the stability of this joint inversion method on a 1D synthetic data set. We also applied the methodology to North Sea multicomponent field data to identify sand layers in a shallow formation. The identified sand layers from our inverted sections are consistent with observations from nearby well logs.

scholarly journals An Ensemble Flow Forecast Method Based on Autoregressive Model and Hydrological Uncertainty Processer

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3138
Author(s):  
Xin Yang ◽  
Jianzhong Zhou ◽  
Wei Fang ◽  
Yurong Wang
Keyword(s):  
Gaussian Mixture ◽  
Decision Makers ◽  
Ar Model ◽  
Model Parameters ◽  
Model Structure ◽  
Forecast Method ◽  
Hydrological Uncertainty ◽  
Actual Operation ◽  
Flow Forecast

In the process of hydrological forecasting, there are uncertainties in data input, model parameters, and model structure, which cause a deterministic forecasting to fail to provide useful risk information to decision-makers. Therefore, the study of ensemble forecasting and the analysis of hydrological uncertainty are of great significance to guide the actual operation of reservoirs in the flood season. This study proposed a Bayesian ensemble forecast method, comprising of a Gaussian mixture model (GMM), a hydrological uncertainty processer (HUP), and an Autoregressive (AR) model. First, the GMM is selected as the marginal distribution function to estimate the uncertainty of observed and modelled data. Next, the AR model is used to correct the forecast rainfall data. Then, a modified HUP is used to deal with the uncertainty of hydrological model structure and rainfall input data. In the end, the ensemble flow forecast results are composed of the expected values of the posterior distribution obtained by HUP under different rainfall conditions. Taking the Three Gorges Reservoir (TGR) as a case study, the ensemble flow prediction in the forecast period is calculated by using the above method. Results show that the method proposed in this paper can improve the accuracy of runoff forecasts and reduce the uncertainty of the hydrological forecast.

scholarly journals An integrated petrophysical-based wedge modeling and thin bed AVO analysis for improved reservoir characterization of Zhujiang Formation, Huizhou sub-basin, China: A case study

2020 ◽  
Vol 12 (1) ◽  
pp. 256-274
Author(s):  
Wasif Saeed ◽  
Hongbing Zhang ◽  
Qiang Guo ◽  
Aamir Ali ◽  
Tahir Azeem ◽  
...  
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Dominant Frequency ◽  
Well Logs ◽  
Amplitude Versus Offset ◽  
Avo Attributes ◽  
Thin Reservoir ◽  
Amplitude Versus

AbstractThe main reservoir in Huizhou sub-basin is Zhujiang Formation of early Miocene age. The petrophysical analysis shows that the Zhujiang Formation contains thin carbonate intervals, which have good hydrocarbon potential. However, the accurate interpretation of thin carbonate intervals is always challenging as conventional seismic interpretation techniques do not provide much success in such cases. In this study, well logs, three-layer forward amplitude versus offset (AVO) model and the wedge model are integrated to analyze the effect of tuning thickness on AVO responses. It is observed that zones having a thickness greater than or equal to 15 m can be delineated with seismic data having a dominant frequency of more than 45 Hz. The results are also successfully verified by analyzing AVO attributes, i.e., intercept and gradient. The study will be helpful to enhance the characterization of thin reservoir intervals and minimize the risk of exploration in the Huizhou sub-basin, China.

Dissolution Facies Window Identification of Carbonate Gas Reservoir by Using Electrical Image Logs: A Case Study from Sichuan Basin

2021 ◽  
Author(s):  
Bing Xie ◽  
Qiang Lai ◽  
Jing Mo ◽  
Li Bai ◽  
Wenjun Luo ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Gas Production ◽  
Gas Reservoir ◽  
Real Gas ◽  
Facies Model ◽  
Dolomite Formation ◽  
Secondary Pore

Abstract Predicted reservoir results from conventional methods didn’t match the production performance in GS B well block in the Lower Sinian Dengying dolomite formation. The predicted gas production of vertical well is around 500k m3/day, but the real gas production is below 100k m3/day. In GS A well block, the predicted gas production of vertical well is consistent with the real gas production around 500k m3/day, and when meter cavie develops, test gas production can reach 1000k m3/day. It suggests the biggest challenge is to clarify reservoir characterization in GS B well block. However, due to the limited resolution of conventional logs and strong heterogeneity of carbonate reservoir, conventional open hole logs and seismic data has limitation to provide the details of secondary pore and fractures to clarify reservoir characterization. The electrical image logs provide high resolution images with high borehole coverage. It can provide abundant information about secondary pore and fracture to identify dominant dissolution facies window. Through electrical image logs, secondary pore and fracture classification in 50 vertical wells were performed in the Lower Sinian Dengying dolomite formation. Five facies were detected based on electrical image logs, including vug facies (honeycomb vug facies, algal stromatolite vug facies and bedding vug facies), cave facies, fracture-vug facies, massive dense facies and dark thin layer dense facies. With the five facies and top interface constraints from seismic data, 3D dissolution facies model was created, which can show different dissolution facies window of GS A and GS B well block. The method in this paper reveals the reason of confliction and agree test gas production. The case study presents how to identify five dissolution facies based on high-resolution electrical image logs with core data calibration. Besides, 3D dissolution facies model is created to show dissolution facies window of GS B well block to optimize well trajectory deployment during the development stage. Better understanding of reservoir characterization was instructive for acid fracturing design of Dengying dolomite gas reservoir as well.

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