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.

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.

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.

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.

High Resolution Reservoir Characterization While Drilling: First Case Study from the Middle East Carbonates with Innovative Multi-Measurement Borehole Imager for Non-Conductive Mud

2020 ◽  
Author(s):  
Janine Maalouf ◽  
Sammy Molua Lyonga ◽  
Sudipan Shasmal ◽  
Humair Ali ◽  
Chandramani Shrivastava ◽  
...  
Keyword(s):  
High Resolution ◽  
Middle East ◽  
Reservoir Characterization ◽  
Measurement Borehole ◽  
First Case

Gas Reservoir Characterization Using Lp-Norm Constrained High-Resolution Seismic Spectral Attributes

2020 ◽  
Vol 177 (11) ◽  
pp. 5417-5433
Author(s):  
Tieyi Wang ◽  
Sanyi Yuan ◽  
Rui Wang ◽  
Shan Yang ◽  
Shangxu Wang
Keyword(s):  
High Resolution ◽  
Reservoir Characterization ◽  
Gas Reservoir ◽  
Lp Norm

On the prediction of settlement from high-resolution shear-wave reflection seismic data: The Trondheim harbour case study, mid Norway

2013 ◽  
Vol 167 ◽  
pp. 72-83 ◽  
Author(s):  
J.S. L'Heureux ◽  
M. Long ◽  
M. Vanneste ◽  
G. Sauvin ◽  
L. Hansen ◽  
...  
Keyword(s):  
High Resolution ◽  
Shear Wave ◽  
Seismic Data ◽  
Wave Reflection ◽  
Reflection Seismic

Evaluation of direct hydrocarbon indicators through comparison of compressional‐ and shear‐wave seismic data: a case study of the Myrnam gas field, Alberta

Geophysics ◽  
1985 ◽  
Vol 50 (1) ◽  
pp. 37-48 ◽  
Author(s):  
Ross Alan Ensley
Keyword(s):  
Shear Wave ◽  
Seismic Data ◽  
Compressional Wave ◽  
Bright Spot ◽  
Low Velocity ◽  
Gas Reservoir ◽  
Gas Field ◽  
Compressional Waves ◽  
The Relationship

Shear waves differ from compressional waves in that their velocity is not significantly affected by changes in the fluid content of a rock. Because of this relationship, a gas‐related compressional‐wave “bright spot” or direct hydrocarbon indicator will have no comparable shear‐wave anomaly. In contrast, a lithology‐related compressional‐wave anomaly will have a corresponding shear‐wave anomaly. Thus, it is possible to use shear‐wave seismic data to evaluate compressional‐wave direct hydrocarbon indicators. This case study presents data from Myrnam, Alberta which exhibit the relationship between compressional‐ and shear‐wave seismic data over a gas reservoir and a low‐velocity coal.

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