Seismic Data Analysis for Well Performance and Production Data Forecast: Massive Low-Relief Gas Reservoir Case Study

2021 ◽  
Author(s):  
Aleksei Anatolyevich Gorlanov ◽  
Dmitrii Yurevich Vorontsov ◽  
Aleksei Sergeevich Schetinin ◽  
Aleksandr Ivanovich Aksenov ◽  
Diana Gennadyevna Ovchinnikova
Keyword(s):  
Seismic Data ◽  
Seismic Attributes ◽  
Gas Production ◽  
Poor Correlation ◽  
Well Performance ◽  
Reservoir Properties ◽  
Gas Reservoir ◽  
Water Contact ◽  
Geological Conditions ◽  
Depth Analysis

Abstract In the process of developing massive gas reservoirs, gas-water contact (GWC) rise is inevitable, which leads to water-breakthrough in wells and declining daily gas production. Drilling horizontal sidetracks and new horizontal wells helps to maintain target production levels. The direction of drilling a horizontal well section largely determines its efficiency. In complex geological conditions, a detailed analysis of seismic data in the drilling area helps to reduce drilling risks and achieve planned starting parameters. The integration of seismic data in geological models is often limited by poor correlation between reservoir properties from wells and seismic attributes. Flow simulation models use seismic data based on the assumptions made by the geological engineers. The study uses a cyclic approach to geological modeling: realizations include in-depth analysis of seismic data and well performance profiles. Modern software modules were used to automatically check the compliance of the geological realization with the development history, as well as to assess the uncertainties. This made it possible to obtain good correlation between well water cut and seismic attributes and to develop a method for determining the presence of shale barriers and "merging windows" of a massive gas reservoir with water-saturated volumes.

Unsupervised machine learning using 3D seismic data applied to reservoir evaluation and rock type identification

2021 ◽  
pp. 1-69
Author(s):  
Marwa Hussein ◽  
Robert R. Stewart ◽  
Deborah Sacrey ◽  
Jonny Wu ◽  
Rajas Athale
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Rock Type ◽  
Seismic Attributes ◽  
Reservoir Quality ◽  
3D Seismic ◽  
Reservoir Properties ◽  
Unsupervised Machine Learning ◽  
Amplitude Data ◽  
3D Seismic Data

Net reservoir discrimination and rock type identification play vital roles in determining reservoir quality, distribution, and identification of stratigraphic baffles for optimizing drilling plans and economic petroleum recovery. Although it is challenging to discriminate small changes in reservoir properties or identify thin stratigraphic barriers below seismic resolution from conventional seismic amplitude data, we have found that seismic attributes aid in defining the reservoir architecture, properties, and stratigraphic baffles. However, analyzing numerous individual attributes is a time-consuming process and may have limitations for revealing small petrophysical changes within a reservoir. Using the Maui 3D seismic data acquired in offshore Taranaki Basin, New Zealand, we generate typical instantaneous and spectral decomposition seismic attributes that are sensitive to lithologic variations and changes in reservoir properties. Using the most common petrophysical and rock typing classification methods, the rock quality and heterogeneity of the C1 Sand reservoir are studied for four wells located within the 3D seismic volume. We find that integrating the geologic content of a combination of eight spectral instantaneous attribute volumes using an unsupervised machine-learning algorithm (self-organizing maps [SOMs]) results in a classification volume that can highlight reservoir distribution and identify stratigraphic baffles by correlating the SOM clusters with discrete net reservoir and flow-unit logs. We find that SOM classification of natural clusters of multiattribute samples in the attribute space is sensitive to subtle changes within the reservoir’s petrophysical properties. We find that SOM clusters appear to be more sensitive to porosity variations compared with lithologic changes within the reservoir. Thus, this method helps us to understand reservoir quality and heterogeneity in addition to illuminating thin reservoirs and stratigraphic baffles.

scholarly journals What Factors Control Shale-Gas Production and Production-Decline Trend in Fractured Systems: A Comprehensive Analysis and Investigation

SPE Journal ◽  
2016 ◽  
Vol 22 (02) ◽  
pp. 562-581 ◽  
Author(s):  
HanYi Wang
Keyword(s):  
Shale Gas ◽  
Gas Production ◽  
Reservoir Model ◽  
Reservoir Properties ◽  
Gas Reservoir ◽  
Gas Well ◽  
Field Development ◽  
Shale Gas Reservoir ◽  
Decline Curve ◽  
Realistic Prediction

Summary One of the most-significant practical problems with the optimization of shale-gas-stimulation design is estimating post-fracture production rate, production decline, and ultimate recovery. Without a realistic prediction of the production-decline trend resulting from a given completion and given reservoir properties, it is impossible to evaluate the economic viability of producing natural gas from shale plays. Traditionally, decline-curve analysis (DCA) is commonly used to predict gas production and its decline trend to determine the estimated ultimate recovery (EUR), but its analysis cannot be used to analyze which factors influence the production-decline trend because of a lack of the underlying support of physics, which makes it difficult to guide completion designs or optimize field development. This study presents a unified shale-gas-reservoir model, which incorporates real-gas transport, nanoflow mechanisms, and geomechanics into a fractured-shale system. This model is used to predict shale-gas production under different reservoir scenarios and investigate which factors control its decline trend. The results and analysis presented in the article provide us with a better understanding of gas production and decline mechanisms in a shale-gas well with certain conditions of the reservoir characteristics. More-in-depth knowledge regarding the effects of factors controlling the behavior of the gas production can help us develop more-reliable models to forecast shale-gas-decline trend and ultimate recovery. This article also reveals that some commonly held beliefs may sound reasonable to infer the production-decline trend, but may not be true in a coupled reservoir system in reality.

Stochastic forecasting of production potential using customised type curves in coal seam gas

2010 ◽  
Vol 50 (2) ◽  
pp. 718 ◽  
Author(s):  
Georg Zangl ◽  
Shripad Biniwale ◽  
Andreas Al-Kinani ◽  
Vikram Sharma ◽  
Rajesh Trivedi
Keyword(s):  
Expert System ◽  
Coal Seam ◽  
Group Membership ◽  
Gas Production ◽  
Well Performance ◽  
Coal Seam Gas ◽  
Reservoir Properties ◽  
Type Curve ◽  
Probability Of Occurrence ◽  
Future Production

This paper discusses a new workflow to stochastically estimate the performance of future production in coal seam gas (CSG) developments. Usually performance evaluations for CSG wells are conducted using either much-generalised statistical methods or numerical simulation. Both approaches have significant drawbacks; the former methods are quick but very often lack accuracy, while the latter is very accurate however also usually highly complex in set-up and computation. The presented workflow is a new approach to well performance prediction that combines speed and reasonable accuracy. The workflow generates a set of key performance indicators of existing wells derived from historic dynamic data (water and gas production rates, pressures, etc.), static data (initial coal and reservoir properties, etc.) and predicted data (simplified production forecasts). The wells are then grouped according to the similarity of their KPIs. The production profiles of the wells within the same group are combined to a type curve that is described by the most likely production profile and an associated uncertainty range. A data-driven expert system is used to identify and capture the correlations of the parameters such as geographic locations, well spacing, reservoir properties and the group membership (equivalent to type curve). This expert system can then be applied to any location in the field in order to determine the most likely group membership of a potential well. The classification of a new well to a group is hereby not necessarily unique; the expert system might classify a new well into several groups and assign a probability of occurrence for each of the groups. A Monte Carlo routine is then applied to forecast the performance of the new well locations honoring the respective probability of occurrence of each type curve.

A statistical methodology for deriving reservoir properties from seismic data

Geophysics ◽  
1995 ◽  
Vol 60 (5) ◽  
pp. 1437-1450 ◽  
Author(s):  
Frédérique Fournier ◽  
Jean‐François Derain
Keyword(s):  
Seismic Data ◽  
Large Scale ◽  
Seismic Attributes ◽  
Seismic Facies ◽  
Reservoir Properties ◽  
Data Set ◽  
Analysis Technique ◽  
Seismic Facies Analysis ◽  
Statistical Relationships

The use of seismic data to better constrain the reservoir model between wells has become an important goal for seismic interpretation. We propose a methodology for deriving soft geologic information from seismic data and discuss its application through a case study in offshore Congo. The methodology combines seismic facies analysis and statistical calibration techniques applied to seismic attributes characterizing the traces at the reservoir level. We built statistical relationships between seismic attributes and reservoir properties from a calibration population consisting of wells and their adjacent traces. The correlation studies are based on the canonical correlation analysis technique, while the statistical model comes from a multivariate regression between the canonical seismic variables and the reservoir properties, whenever they are predictable. In the case study, we predicted estimates and associated uncertainties on the lithofacies thicknesses cumulated over the reservoir interval from the seismic information. We carried out a seismic facies identification and compared the geological prediction results in the cases of a calibration on the whole data set and a calibration done independently on the traces (and wells) related to each seismic facies. The later approach produces a significant improvement in the geological estimation from the seismic information, mainly because the large scale geological variations (and associated seismic ones) over the field can be accounted for.

Delineation of reservoir channels by different seismic attributes and geobody extractions for robust volumetric estimation, Saffron Field, offshore Nile Delta, Egypt

2021 ◽  
Vol 40 (7) ◽  
pp. 484-493
Author(s):  
Doha Monier ◽  
Azza El Rawy ◽  
Abdullah Mahmoud
Keyword(s):  
Seismic Data ◽  
Nile Delta ◽  
Three Dimensional ◽  
Dynamic Test ◽  
Data Interpretation ◽  
Seismic Attributes ◽  
Reservoir Properties ◽  
Root Mean Square Amplitude ◽  
Seismic Attribute ◽  
Attribute Analysis

The Nile Delta Basin is a major gas province. Commercial gas discoveries there have been proven mainly in Pleistocene to Oligocene sediments, and most discoveries are within sandstone reservoirs. Three-dimensional seismic data acquired over the basin have helped greatly in imaging and visualization of stratigraphy and structure, leading to robust understanding of the subsurface. Channel fairways serve as potential reservoir units; hence, mapping channel surfaces and identifying and defining infill lithology is important. Predicting sand distribution and reservoir presence is one of the key tasks as well as one of the key uncertainties in exploration. Integrating state-of-the-art technologies, such as including 3D seismic reflection surveys, seismic attributes, and geobody extractions, can reduce this uncertainty through recognition and accurate mapping of channel features. In this study, seismic attribute analysis, frequency analysis through spectral decomposition (SD), geobodies, and seismic sections have been used to delineate shallow Plio-Pleistocene El Wastani Formation channel fairways within the Saffron Field, offshore Nile Delta, Egypt. This has led to providing more reliable inputs for calculation of volumetrics. Interpretation of the stacked-channels complex through different seismic attributes helped to discriminate between sand-filled and shale-filled channels and in understanding their geometries. Results include more confident delineation of four distinct low-sinuosity channelized features. Petrophysical evaluation conducted on five wells penetrating Saffron reservoirs included electric logs and modular dynamic test data interpretation. The calculated average reservoir properties were used in different volumetric calculation cases. Different approaches were applied to delineate channel geometries that were later used in performing different volumetric cases. These approaches included defining channels from root-mean-square amplitude extractions, SD color-blended frequencies, and geobodies, all calculated from prestack seismic data. The different volumetric cases performed were compared against the latest field volume estimates proven after several years of production in which an area-versus-depth input showed the closest calculated hydrocarbon volumes to the actual proven field volumes.

Specifics of Implemented Development System of Shtormove Field Black Sea Offshore

2021 ◽  
Author(s):  
Mykhailo Mykytovych Bahniuk ◽  
Vitaliy Mykolaiovych Vladyka ◽  
Oleh Stepanovych Hotsynets ◽  
Oleksandra Olehivna Dmyshko ◽  
Maksym Volodymyrovych Dorokhov ◽  
...  
Keyword(s):  
Estimation Method ◽  
Gas Production ◽  
Gas Condensate ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Well Testing ◽  
Reservoir Properties ◽  
Water Contact ◽  
Thin Sections ◽  
Development System

Abstract The development system of the main hydrocarbon deposit of the Shtormove field was designed taking into account the change in reservoir rock properties in horizontal and vertical sections of the gas-saturated interval. Based on the results of core analyses and interpretation of logging data from exploration and appraisal wells, maximum porosity values were assigned to the top part of the deposit, with lower porosity values assigned closer to the gas-water contact area. The analyzed thin sections demonstrated vertical and subvertical fractures, as well as multiple pores and dissolution vugs. Most of the fractures occur in the top part of the gas-condensate deposit. Natural fractures in the rocks of the productive interval were confirmed by well testing using a steady flow analysis. Taking into account the determined reservoir properties’ distribution pattern in the gas-condensate deposit II, the wells were drilled in the top part of the deposit from a fixed offshore platform during the pilot development period. During this period, the estimated recoverable gas reserves’ values matched the values obtained using the volumetric estimation method. In the following years, in order to increase the gas recovery rate, the infilling production wells were drilled in the top part of the deposit. Based on the analysis of the development, it was determined that the addition of new wells had little effect on the performance of existing ones. As a result, attributable to infill drilling in the top part of the deposit, the annual gas production increased. Given the similarity of the geological model and distribution of reservoir properties, the implemented development system of the Shtormove field should be recommended for new development targets.

Prediction of Reservoir Properties from Seismic Data by Multivariate Geostatistics Analysis

2021 ◽  
Author(s):  
Vladimir V. Bezkhodarnov ◽  
Tatiana I. Chichinina ◽  
Mikhail O. Korovin ◽  
Valeriy V. Trushkin
Keyword(s):  
Seismic Data ◽  
Quantitative Estimation ◽  
Clay Content ◽  
Seismic Attributes ◽  
Information Value ◽  
Seismic Exploration ◽  
Rock Properties ◽  
Regression Equations ◽  
Reservoir Properties ◽  
Multivariate Geostatistics

Abstract A new technique has been developed and is being improved, which allows, on the basis of probabilistic and statistical analysis of seismic data, to predict and evaluate the most important parameters of rock properties (including the reservoir properties such as porosity and permeability), that is, oil saturation, effective thicknesses of reservoirs, their sand content, clay content of seals, and others; it is designed to predict the reservoir properties with sufficient accuracy and detail, for subsequent consideration of these estimates when evaluating hydrocarbon reserves and justifying projects for the deposits development. Quantitative reservoir-property prediction is carried out in the following stages: –Optimization of the graph ("scenario") of seismic data processing to solve not only the traditional structural problem of seismic exploration, but also the parametric one that is, the quantitative estimation of rock properties.–Computation of seismic attributes, including exclusive ones, not provided for in existing interpretation software packages.–Estimation of reservoir properties from well logs as the base data.–Multivariate correlation and regression analysis (MCRA) includes the following two stages: Establishing correlations of seismic attributes with estimates of rock properties obtained from well logs.Construction of multidimensional (multiple) regression equations with an assessment of the "information value" of seismic attributes and the reliability of the resulting predictive equations. (By the "informative value" we mean the informativeness quality of the attribute.)–Computation and construction of the forecast map variants, their analysis and producing the resultant map (as the most optimal map version) for each predicted parameter.–Obtaining the resultant forecast maps with their zoning according to the degree of the forecast reliability. The MCRA technique is tested by production and prospecting trusts during exploration and reserves’ estimation of several dozen fields in Western Siberia: Kulginskoye, Shirotnoye, Yuzhno-Tambaevskoye, etc. (Tomsk Geophysical Trust, 1997-2002); Dvurechenskoe, Zapadno-Moiseevskoe, Talovoe, Krapivinskoe, Ontonigayskoe, etc. (TomskNIPIneft, 2002–2013).

Seismic attributes and kinematic azimuthal analysis for fracture and stress detection in complex geologic settings

2014 ◽  
Vol 2 (1) ◽  
pp. SA67-SA75 ◽  
Author(s):  
Krzysztof (Kris) Sliz ◽  
Saleh Al-Dossary
Keyword(s):  
Seismic Data ◽  
Seismic Attributes ◽  
Small Scale ◽  
Seismic Velocities ◽  
Stress Detection ◽  
Fracture Detection ◽  
Reservoir Properties ◽  
Horizontal Drilling ◽  
Compressional Stress ◽  
Orientation Density

Fractured rocks can exhibit good reservoir properties and provide high-permeability passages for hydrocarbons. Understanding fracture and stress systems is a key element in successful horizontal drilling and fracking for unconventional reservoir exploration. As a result, there is growing interest in methods that can estimate fracture orientation, density, and style. However, fracture detection using surface seismic data is challenging, and the results are usually ambiguous. Each method has its own strengths and weaknesses and responds to fractures and compressional stress in different ways. A major uncertainty in fracture analysis based on azimuthally variant seismic velocities is caused by interference from structural effects, localized small-scale velocity anomalies, and directional stress. They can induce azimuthal variation in velocity, which can mask the influence on traveltimes caused by the fractures. To overcome these challenges, we focused on a fracture and compressional stress detection methodology using 3D scanning of azimuthally dependent residual moveout volumes constrained by fracture-sensitive seismic attributes. Our workflow was successfully applied to wide-azimuth, highfold land seismic data acquired over a fractured formation in the northern part of Saudi Arabia, where we were able to map 3D zones with a high probability of fractures and differentiate them from areas with higher compressional stress.

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