Integrated shale-gas reservoir characterization: A case study incorporating multicomponent seismic data

2018 ◽  
Vol 6 (2) ◽  
pp. SE23-SE37
Author(s):  
Laurie M. Weston Bellman
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Data Interpretation ◽  
Seismic Profile ◽  
Gas Production ◽  
Seismic Survey ◽  
Reservoir Properties ◽  
Converted Wave ◽  
Multistage Hydraulic Fracturing

The objective of this case study is to predict geologic properties of a shale reservoir interval to guide production and completion planning for successful development of the reservoir. The conditioning, analysis, and blending of the converted-wave (PS) seismic data into a quantitative interpretation (QI) workflow are described in detail, illustrating the successful integration of geologic information and multiple seismic attributes. A multicomponent 3D seismic survey, several wells with dipole sonic logs, and a multicomponent (3C) 3D vertical seismic profile are available for the study. For comparisons of the incremental value of PS data, the QI workflow is completed entirely using only PP data and then modified and redone to incorporate information from the PS data. Predictions of the geologic properties for both workflows are assessed for accuracy against the existing well log and core evidence. Determining reservoir properties of the shale units of interest is important to the successful placement of horizontal wells for efficient multistage hydraulic fracturing and maximum gas production. Although conventional interpretation of conventional seismic data can only provide reservoir geometry, the quantitative analysis of prestack multicomponent data in this study reveals detailed distinctions between reservoir units and relative measures of porosity and brittleness bulk properties within each unit. Using all of the elastic properties derived from the seismic data analysis allowed for the classification of lithological units, which were, in turn, subclassified based on unit-specific reservoir properties. The upper reservoir units (Muskwa and Otter Park) were shown to have more variability in brittleness than the lower reservoir unit (Evie). Validation at a reliable well control confirmed these distinctive units and properties to be very high resolution and accurate, particularly when the PS data were incorporated into the workflow. The results of this method of analysis provided significantly more useful information for appraisal and development decisions than conventional seismic data interpretation alone.

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.

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.

Seismic solutions utilizing existing in-mine infrastructure for mineral exploration: A case study from Maseve platinum mine, South Africa

2022 ◽  
Vol 41 (1) ◽  
pp. 54-61
Author(s):  
Moyagabo K. Rapetsoa ◽  
Musa S. D. Manzi ◽  
Mpofana Sihoyiya ◽  
Michael Westgate ◽  
Phumlani Kubeka ◽  
...  
Keyword(s):  
South Africa ◽  
Seismic Data ◽  
Mineral Exploration ◽  
Ground Surface ◽  
Seismic Survey ◽  
Noisy Environment ◽  
Borehole Data ◽  
Passive Seismic ◽  
Below Ground

We demonstrate the application of seismic methods using in-mine infrastructure such as exploration tunnels to image platinum deposits and geologic structures using different acquisition configurations. In 2020, seismic experiments were conducted underground at the Maseve platinum mine in the Bushveld Complex of South Africa. These seismic experiments were part of the Advanced Orebody Knowledge project titled “Developing technologies that will be used to obtain information ahead of the mine face.” In these experiments, we recorded active and passive seismic data using surface nodal arrays and an in-mine seismic land streamer. We focus on analyzing only the in-mine active seismic portion of the survey. The tunnel seismic survey consisted of seven 2D profiles in exploration tunnels, located approximately 550 m below ground surface and a few meters above known platinum deposits. A careful data-processing approach was adopted to enhance high-quality reflections and suppress infrastructure-generated noise. Despite challenges presented by the in-mine noisy environment, we successfully imaged the platinum deposits with the aid of borehole data and geologic models. The results open opportunities to adapt surface-based geophysical instruments to address challenging in-mine environments for mineral exploration.

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.

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.

PRACTICAL USE OF BOREHOLE SEISMIC IN EXPLORATION

1984 ◽  
Vol 24 (1) ◽  
pp. 429
Author(s):  
F. Sandnes W. L. Nutt ◽  
S. G. Henry
Keyword(s):  
Seismic Data ◽  
Seismic Profile ◽  
Seismic Survey ◽  
Vertical Seismic Profile ◽  
Direct Signal ◽  
Seismic Fault ◽  
Time Calibration ◽  
Vsp Data ◽  
Borehole Seismic ◽  
Processing Techniques

The improvement of acquisition and processing techniques has made it possible to study seismic wavetrains in boreholes.With careful acquisition procedures and quantitative data processing, one can extract useful information on the propagation of seismic events through the earth, on generation of multiples and on the different reflections coming from horizons that may not all be accessible by surface seismic.An extensive borehole seismic survey was conducted in a well in Conoco's contract area 'Block B' in the South China Sea. Shots at 96 levels were recorded, and the resulting Vertical Seismic Profile (VSP) was carefully processed and analyzed together with the Synthetic Seismogram (Geogram*) and the Synthetic Vertical Seismic Profile (Synthetic VSP).In addition to the general interpretation of the VSP data, i.e. time calibration of surface seismic, fault identification, VSP trace inversion and VSP Direct Signal Analysis, the practical inclusion of VSP data in the reprocessing of surface seismic data was studied. Conclusions that can be drawn are that deconvolution of surface seismic data using VSP data must be carefully approached and that VSP can be successfully used to examine phase relationships in seismic data.

An integrated surface seismic/seismic profile case study: Simonette area, Alberta

Geophysics ◽  
1993 ◽  
Vol 58 (11) ◽  
pp. 1676-1688
Author(s):  
Ronald C. Hinds ◽  
Neil L. Anderson ◽  
Richard Kuzmiski
Keyword(s):  
Seismic Data ◽  
Cost Effective ◽  
Seismic Profile ◽  
Viable Alternative ◽  
Target Area ◽  
Vertical Seismic Profile ◽  
Subsurface Geology ◽  
Well Location ◽  
Vsp Data

On the basis of conventional surface seismic data, the 13–15–63–25W5M exploratory well was drilled into a low‐relief Leduc Formation reef (Devonian Woodbend Group) in the Simonette area, west‐central Alberta, Canada. The well was expected to intersect the crest of the reef and encounter about 50–60 m of pay; unfortunately it was drilled into a flank position and abandoned. The decision to abandon the well, as opposed to whipstocking in the direction of the reef crest, was made after the acquisition and interpretive processing of both near( and far‐offset (252 and 524 m, respectively) vertical seismic profile (VSP) data, and after the reanalysis of existing surface seismic data. The near‐ and far‐offset VSPs were run and interpreted while the drill rig remained on‐site, with the immediate objectives of: (1) determining an accurate tie between the surface seismic data and the subsurface geology; and (2) mapping relief along the top of the reef over a distance of 150 m from the 13–15 well location in the direction of the adjacent productive 16–16 well (with a view to whipstocking). These surveys proved to be cost‐effective in that the operators were able to determine that the crest of the reef was out of the target area, and that whipstocking was not a viable alternative. The use of VSP surveys in this situation allowed the operators to avoid the costs associated with whipstocking, and to feel confident with their decision to abandon the well.

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