scholarly journals Enhancing Paleoreef Reservoir Characterization through Machine Learning and Multi-Attribute Seismic Analysis: Silurian Reef Examples from the Michigan Basin

Geosciences ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 142
Author(s):  
Carl Buist ◽  
Heather Bedle ◽  
Matthew Rine ◽  
John Pigott
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Spectral Decomposition ◽  
Reservoir Characterization ◽  
Seismic Analysis ◽  
Strong Relationship ◽  
Analysis Data ◽  
Average Frequency ◽  
High Porosity ◽  
Michigan Basin

Historically, Silurian pinnacle reef complexes in the Michigan Basin have been largely identified using 2D seismic with very little research on the reservoir characterization of these reefs using 3D seismic data. By incorporating a high-resolution 3D dataset constrained by a well-studied and data-rich paleoreef reservoir, the Puttygut reef, seismic attributes were correlated to petrophysical properties through machine learning and self-organizing maps (SOMs). A suite of structural and frequency-based attributes was calculated from pre-stack time migrated (PSTM) seismic data, with only a subset of them selected as SOM inputs. Structural attributes enhanced details in the reef but frequency attributes were overall more useful for correlating with reservoir quality. A strong relationship between certain combination percentages of attributes and certain sections of the reef with porosity and permeability was found after the SOM results were compared to wireline log and core analysis data. Areas with high permeability and porosity correlated with the average frequency and spectral decomposition at 29 and 81 Hz. Areas with high porosity and varying permeability correlated with the average frequency and spectral decomposition at 29, 57, and 81 Hz. Areas with intermediate porosity correlated with the average frequency and spectral decomposition at 29 and 57 Hz. The efficacy of the procedure was then demonstrated on two nearby reefs with very similar results.

Seismic Qualitative and Quantitative Analysis for Reservoir Characterization of Globigerina BH Gas Field, North East Java Basin.

2021 ◽  
Author(s):  
I. Sumantri
Keyword(s):  
Quantitative Analysis ◽  
Seismic Data ◽  
Quantitative Methods ◽  
Reservoir Characterization ◽  
Seismic Analysis ◽  
Analysis Model ◽  
Gas Reservoir ◽  
Qualitative And Quantitative ◽  
North East ◽  
Exploration Well

BH field is one of the Globigerina limestone gas reservoir that exhibits strong seismic direct hydrocarbon indicator (DHI). This field is a 4-way dip faulted closure with Globigerina limestone as the main reservoir objective. The field was discovered back in 2011 by BH-1 exploration well and successfully penetrated about 350ft gross gas pay. BH-1 well was plugged and abandoned as Pliocene Globigerina limestone Mundu-Selorejo sequence gas discoveries. The laboratory analysis of sampled gas consists of 97.8% of CH4 and indicating a biogenic type of gas. This is the only exploration well drilled in this field and located on the crest of the structure. Seismic analysis both qualitative and quantitative, are common tools in delineating and characterizing reservoir. These methods usually make use of seismic data and well log collaboratively in the quest to reveal reservoir features internally. The lack of appraisal well in the area of study made the reservoir characterization process must be carried out thoroughly, incorporating several seismic datasets, both PSTM and PSDM, seismic gathers and stacks. Bounded by appraisal well limitation, this research looks into Gassmann's fluid substitution modeling, seismic forward modeling to confirm the DHI flat spot presence in the seismic, as well as seismic AVO analysis. Meanwhile, for quantitative analysis, model-based seismic post-stack inversion and simultaneous seismic pre-stack inversion were conducted in order to delineate the distribution of Globigerina limestone gas reservoir in BH Field. Through comprehensive analyses of qualitative and quantitative methods, this research may answer the challenge on how to intensively utilize seismic data to compensate the lack of appraisal well data in order to keep delivering a proper subsurface reservoir delineation.

Applications of quantitative prestack seismic analysis to unconventional resource play characterization in the Permian/Delaware Basin

2019 ◽  
Vol 38 (2) ◽  
pp. 106-115 ◽  
Author(s):  
Phuong Hoang ◽  
Arcangelo Sena ◽  
Benjamin Lascaud
Keyword(s):  
Hydraulic Fracturing ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Seismic Analysis ◽  
Seismic Inversion ◽  
Reservoir Properties ◽  
Yield Attributes ◽  
Horizontal Drilling ◽  
Delaware Basin ◽  
Unconventional Resource

The characterization of shale plays involves an understanding of tectonic history, geologic settings, reservoir properties, and the in-situ stresses of the potential producing zones in the subsurface. The associated hydrocarbons are generally recovered by horizontal drilling and hydraulic fracturing. Historically, seismic data have been used mainly for structural interpretation of the shale reservoirs. A primary benefit of surface seismic has been the ability to locate and avoid drilling into shallow carbonate karsting zones, salt structures, and basement-related major faults which adversely affect the ability to drill and complete the well effectively. More recent advances in prestack seismic data analysis yield attributes that appear to be correlated to formation lithology, rock strength, and stress fields. From these, we may infer preferential drilling locations or sweet spots. Knowledge and proper utilization of these attributes may prove valuable in the optimization of drilling and completion activities. In recent years, geophysical data have played an increasing role in supporting well planning, hydraulic fracturing, well stacking, and spacing. We have implemented an integrated workflow combining prestack seismic inversion and multiattribute analysis, microseismic data, well-log data, and geologic modeling to demonstrate key applications of quantitative seismic analysis utilized in developing ConocoPhillips' acreage in the Delaware Basin located in Texas. These applications range from reservoir characterization to well planning/execution, stacking/spacing optimization, and saltwater disposal. We show that multidisciplinary technology integration is the key for success in unconventional play exploration and development.

Seismic characterization of a Mississippi Lime resource play in Osage County, Oklahoma, USA

2013 ◽  
Vol 1 (2) ◽  
pp. SB97-SB108 ◽  
Author(s):  
Benjamin L. Dowdell ◽  
J. Tim Kwiatkowski ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Seismic Analysis ◽  
High Porosity ◽  
Fracture Density ◽  
Coherent Noise ◽  
High Fracture ◽  
Data Set ◽  
Horizontal Drilling ◽  
Prestack Inversion ◽  
Osage County

With the advent of horizontal drilling and hydraulic fracturing in the Midcontinent, USA, fields once thought to be exhausted are now experiencing renewed exploitation. However, traditional Midcontinent seismic analysis techniques no longer provide satisfactory reservoir characterization for these unconventional plays; new seismic analysis methods are needed to properly characterize these radically innovative play concepts. Time processing and filtering is applied to a raw 3D seismic data set from Osage County, Oklahoma, paying careful attention to velocity analysis, residual statics, and coherent noise filtering. The use of a robust prestack structure-oriented filter and spectral whitening greatly enhances the results. After prestack time migrating the data using a Kirchhoff algorithm, new velocities are picked. A final normal moveout correction is applied using the new velocities, followed by a final prestack structure-oriented filter and spectral whitening. Simultaneous prestack inversion uses the reprocessed and time-migrated seismic data as input, along with a well from within the bounds of the survey. With offsets out to 3048 m and a target depth of approximately 880 m, we can invert for density in addition to P- and S-impedance. Prestack inversion attributes are sensitive to lithology and porosity while surface seismic attributes such as coherence and curvature are sensitive to lateral changes in waveform and structure. We use these attributes in conjunction with interpreted horizontal image logs to identify zones of high porosity and high fracture density.

Fluids and lithofacies prediction based on integration of well-log data and seismic inversion: a machine learning approach

Geophysics ◽  
2021 ◽  
pp. 1-67
Author(s):  
Luanxiao Zhao ◽  
Caifeng Zou ◽  
Yuanyuan Chen ◽  
Wenlong Shen ◽  
Yirong Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Domain Knowledge ◽  
Reservoir Characterization ◽  
Model Building ◽  
Imbalanced Data ◽  
Seismic Inversion ◽  
Well Log ◽  
Spatial Constraints ◽  
Imbalanced Data Sets

Seismic prediction of fluid and lithofacies distributions is of great interest to reservoir characterization, geological model building, and flow unit delineation. Inferring fluids and lithofacies from seismic data under the framework of machine learning is commonly subject to issues of limited features, imbalanced data sets, and spatial constraints. As a consequence, an XGBoost based workflow, which takes feature engineering, data balancing, and spatial constraints into account, is proposed to predict the fluid and lithofacies distribution by integrating well-log and seismic data. The constructed feature set based on simple mathematical operations and domain knowledge outperforms the benchmark group consisting of conventional elastic attributes of P-impedance and Vp/Vs ratio. A radial basis function characterizing the weights of training samples according to the distances from the available wells to the target region is developed to impose spatial constraints on the model training process, significantly improving the prediction accuracy and reliability of gas sandstone. The strategy combining the synthetic minority oversampling technique (SMOTE) and spatial constraints further increases the F1 score of gas sandstone and also benefits the overall prediction performance of all the facies. The application of the combined strategy on prestack seismic inversion results generates a more geologically reasonable spatial distribution of fluids, thus verifying the robustness and effectiveness of the proposed workflow.

scholarly journals Unsupervised machine learning for time-lapse seismic studies and reservoir monitoring

2021 ◽  
pp. 1-59
Author(s):  
Marwa Hussein ◽  
Robert R. Stewart ◽  
Deborah Sacrey ◽  
David H. Johnston ◽  
Jonny Wu
Keyword(s):  
Machine Learning ◽  
Reservoir Simulation ◽  
Seismic Data ◽  
Water Saturation ◽  
Seismic Analysis ◽  
Time Lapse ◽  
Seismic Attributes ◽  
Rock Properties ◽  
Unsupervised Machine Learning ◽  
4D Seismic

Time-lapse (4D) seismic analysis plays a vital role in reservoir management and reservoir simulation model updates. However, 4D seismic data are subject to interference and tuning effects. Being able to resolve and monitor thin reservoirs of different quality can aid in optimizing infill drilling or locating bypassed hydrocarbons. Using 4D seismic data from the Maui field in the offshore Taranaki basin of New Zealand, we generate typical seismic attributes sensitive to reservoir thickness and rock properties. We find that spectral instantaneous attributes extracted from time-lapse seismic data illuminate more detailed reservoir features compared to those same attributes computed on broadband seismic data. We develop an unsupervised machine learning workflow that enables us to combine eight spectral instantaneous seismic attributes into single classification volumes for the baseline and monitor surveys using self-organizing maps (SOM). Changes in the SOM natural clusters between the baseline and monitor surveys suggest production-related changes that are caused primarily by water replacing gas as the reservoir is being swept under a strong water drive. The classification volumes also facilitate monitoring water saturation changes within thin reservoirs (ranging from very good to poor quality) as well as illuminating thin baffles. Thus, these SOM classification volumes show internal reservoir heterogeneity that can be incorporated into reservoir simulation models. Using meaningful SOM clusters, geobodies are generated for the baseline and monitor SOM classifications. The recoverable gas reserves for those geobodies are then computed and compared to production data. The SOM classifications of the Maui 4D seismic data seems to be sensitive to water saturation change and subtle pressure depletions due to gas production under a strong water drive.

A nonlinear solution to 3D seismic data conditioning using trained dictionaries

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. V397-V406
Author(s):  
Zhou Yu ◽  
Rodney Johnston ◽  
John Etgen ◽  
Anya Reitz
Keyword(s):  
Wavelet Transform ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Seismic Analysis ◽  
Signal To Noise Ratio ◽  
Nonlinear Approximation ◽  
Complex Wavelet Transform ◽  
Amplitude Variation With Offset ◽  
User Input ◽  
Complex Wavelet

Seismic analysis for reservoir characterization has been a primary focus for the geophysical community for decades. One of the critical steps in delivering high-quality processed seismic data for seismic analysis is to remove undesirable prestack seismic phenomena prior to amplitude variation with offset (AVO) analysis. Contrary to the conventional approach, which is mainly 2D gather-based and assumes flat events, we have developed a 3D nonlinear approach with a single principle: the 3D geologic structure should be invariant from offset to offset. Trained dictionaries, generated by 3D complex wavelet transformation over pilot volumes, are progressively constructed by stacking over selected offsets or angles. A sparse nonlinear approximation using the L0 norm is imposed on the data against the trained dictionaries after applying a 3D complex wavelet transform to the data. The final step is to apply an inverse 3D complex wavelet transform to the sparsified coefficients to return to the data space. This workflow is repeated for all offsets or angles. The workflow is automatic and requires minimal user input, resulting in a fast and efficient process. Multiple field data examples have demonstrated significant signal-to-noise ratio uplift, AVO and azimuthal AVO conservation, preservation of steeply dipping structural events, and multiple suppression. The processing time is significantly shorter compared with alternative conventional processes.

High-frequency anomalies in carbonate reservoir characterization using spectral decomposition

Geophysics ◽  
2011 ◽  
Vol 76 (3) ◽  
pp. V47-V57 ◽  
Author(s):  
Yandong Li ◽  
Xiaodong Zheng ◽  
Yan Zhang
Keyword(s):  
High Frequency ◽  
Spectral Decomposition ◽  
Reservoir Characterization ◽  
Low Frequency ◽  
Carbonate Reservoir ◽  
Type I ◽  
High Porosity ◽  
Type Ii ◽  
Type Iii ◽  
Carbonate Reservoir Characterization

Low-frequency shadows have often been used as hydrocarbon indicators in the application of spectral decomposition. The reason behind the low-frequency anomaly has been explained as high-frequency energy attenuation caused by hydrocarbons. However, in our practice on carbonate reservoir characterization in two areas, Precaspian Basin and Central Tarim Basin, China, we encountered high-frequency anomalies, i.e., the isofrequency slices or sections at high frequencies exhibit anomalies associated with the good carbonate reservoir, particularly in the tight limestone background. We used the product of porosity and thickness as a parameter to measure the quality of the carbonate reservoir of each well and classified the 46 wells in our two studied areas into three types. Type I wells contain high-porosity thick reservoirs, type II wells contain reservoirs with moderate porosity and thickness, and type III wells contain only low-porosity thin reservoirs. The results were that 12 out of 13 type I wells exhibit high-frequency anomalies, and 30 out of 33 type II and type III wells do not exhibit high-frequency anomalies. We further validated the existence of this high-frequency anomaly by forward modeling analysis and fluid substitution experiments using the actual well-log curves measured in the carbonate reservoir. The results showed that in our two studied areas the high-frequency anomalies are more common than low-frequency shadows that can be observed only when the thickness of the reservoir is more than half of the wavelength or the reservoir rocks are extremely unconsolidated. Therefore, this high-frequency anomaly may be used as a more reliable indicator for a good carbonate reservoir than low-frequency shadows in real applications.

The influence of volcanic rocks on the characterization of Rosebank Field – new insights from ocean-bottom seismic data and geological analogues integrated through interpretation and modelling

2016 ◽  
Vol 8 (1) ◽  
pp. 373-384 ◽  
Author(s):  
S. Poppitt ◽  
L. J. Duncan ◽  
B. Preu ◽  
F. Fazzari ◽  
J. Archer
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Seismic Analysis ◽  
Volcanic Rocks ◽  
Flood Basalt ◽  
Ocean Bottom ◽  
Volcanic System ◽  
Field Development ◽  
Velocity Models ◽  
Depth Prediction

AbstractDuring Late Paleocene–Early Eocene times, the modern Rosebank structure was located at the juxtaposition of the easterly advancing Flett volcanic system and the northerly prograding Flett delta. As a result, the Rosebank reservoir sandstones are interstratified with volcanic and volcaniclastic rocks, offering challenges for reservoir imaging, depth prediction and reservoir characterization. These challenges have driven the application of Ocean Bottom Node (OBN) seismic technology. OBN data have yielded improved velocity models for depth conversion, better reservoir definition and key insights to aid the modelling of sand distribution from seismic attributes. Spectral decomposition of the OBN seismic data has facilitated the extraction of distinct volcanic subunits, whilst spectral enhancement has enabled visualization of complex stacking patterns within individual igneous layers. To complement the seismic analysis, detailed geological analogue studies have been undertaken in volcanic provinces such as the Palaeogene volcanic district of SE Greenland and the Columbia River Flood Basalt Province, USA. No single outcrop provides a definitive analogy to Rosebank, but each offers insights that provide an important link to understanding and managing the main subsurface uncertainties associated with field development. Integration of these multiple workflows have improved the reservoir characterization and provided the foundation for the optimization of the field development plan.

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