scholarly journals Reservoir modeling of New Albany Shale

2010 ◽  
Author(s):  
◽  
Amirmasoud Kalantari-Dahaghi ◽  
Keyword(s):  
Gas Production ◽  
Natural Fracture ◽  
Reservoir Modeling ◽  
Production Data ◽  
Reservoir Model ◽  
Top Down ◽  
New Albany Shale ◽  
Production History ◽  
The Impact ◽  
Western Kentucky

The intent of this study is to reassess the potential of New Albany Shale formation using a novel and integrated workflow, which incorporates field production data and well logs using a series of traditional reservoir engineering analyses complemented by artificial intelligence & data mining techniques. The model developed using this technology is a full filed model and its objective is to predict future reservoir/well performance in order to recommend field development strategies.;The impact of different reservoir characteristics such as matrix porosity, matrix permeability, initial reservoir pressure and pay thickness as well as the length and the orientation of horizontal wells on gas production in New Albany Shale have been presented.;The study was conducted using publicly available numerical model, specifically developed to simulate gas production from naturally fractured reservoirs.;The study focuses on several New Albany Shale (NAS) wells in Western Kentucky. Production from these wells is analyzed and history matched. During the history matching process, natural fracture length, density and orientations as well as fracture bedding of the New Albany Shale are modeled.;Sensitivity analysis is performed to identify the impact of reservoir characteristics and natural fracture aperture, density and length on gas production, using information found in the literature and outcrops and by performing sensitivity analysis on key reservoir and fracture parameters.;Then, the history-matched results of 87 NAS wells have been used to develop a full field reservoir model using an integrated workflow, named Top-Down, Intelligent Reservoir Modeling. In this integrated workflow unlike traditional reservoir simulation and modeling, we do not start from building a geo-cellular model. Top-Down intelligent reservoir modeling starts by analyzing the production data using traditional reservoir engineering techniques such as Decline Curve Analysis, Type Curve Matching, Single-well History Matching, Volumetric Reserve Estimation and Recovery Factor. These analyses are performed on individual wells in a multi-well New Albany Shale gas reservoir in Western Kentucky that has a reasonable production history. Data driven techniques are used to develop single-well predictive models from the production history and the well logs (and any other available geologic and petrophysical data).;Upon completion of the abovementioned analyses a large database is generated. This database includes a large number of spatio-temporal snap shots of reservoir behavior. Artificial intelligence and data mining techniques are used to fuse all these information into a cohesive reservoir model. The reservoir model is calibrated (history matched) using the production history of the most recent set of wells that have been drilled in the field. The calibrated reservoir model is utilized for predictive purposes to identify the most effective field development strategies including locations of infill wells, remaining reserves, and under-performer wells. Capabilities of this new technique, ease of use and much shorter development and analysis time are advantages of Top-Down modeling as compared to the traditional simulation and modeling.;In addition, 31 recently drilled well in Christian county Western Kentucky-Halley's Mills quadrangle have been used to perform Top-down modeling. Zone manager feature of Geographix software is used. The available production data are going to be the attributes in this feature. The contours are generated and the results have been compared with the result of Top-down modeling (Fuzzy pattern recognition). Structural map, isopach map and the other geological map has been generated using Geographix.;Additionally, in order to indentify the effect of horizontal lateral length on well productivity from New Albany Shale, fracture network has been regenerated in order to represent the distribution of natural fracture in that formation.

scholarly journals Development of Shale Gas Prediction Models for Long-Term Production and Economics Based on Early Production Data in Barnett Reservoir

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 424 ◽  
Author(s):  
Viet Nguyen-Le ◽  
Hyundon Shin ◽  
Edward Little
Keyword(s):  
Shale Gas ◽  
Net Present Value ◽  
Prediction Models ◽  
Input Parameter ◽  
Gas Production ◽  
Barnett Shale ◽  
Production Data ◽  
Production History ◽  
Early Production

This study examined the relationship between the early production data and the long-term performance of shale gas wells, including the estimated ultimate recovery (EUR) and economics. The investigated early production data are peak gas production rate, 3-, 6-, 12-, 18-, and 24-month cumulative gas production (CGP). Based on production data analysis of 485 reservoir simulation datasets, CGP at 12 months (CGP_12m) was selected as a key input parameter to predict a long-term shale gas well’s performance in terms of the EUR and net present value (NPV) for a given well. The developed prediction models were then validated using the field production data from 164 wells which have more than 10 years of production history in Barnett Shale, USA. The validation results showed strong correlations between the predicted data and field data. This suggests that the proposed models can predict the shale gas production and economics reliably in Barnett shale area. Only a short history of production (one year) can be used to estimate the EUR and NPV of various production periods for a gas well. Moreover, the proposed prediction models are consistently applied for young wells with short production histories and lack of reservoir and hydraulic fracturing data.

scholarly journals Ensemble-Based Seismic and Production Data Assimilation Using Selection Kalman Model

2021 ◽  
Author(s):  
Maxime Conjard ◽  
Dario Grana
Keyword(s):  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Ensemble Methods ◽  
Measured Data ◽  
Reservoir Modeling ◽  
Production Data ◽  
Model Parameters ◽  
Reservoir Model ◽  
Ensemble Smoother ◽  
Porosity And Permeability

AbstractData assimilation in reservoir modeling often involves model variables that are multimodal, such as porosity and permeability. Well established data assimilation methods such as ensemble Kalman filter and ensemble smoother approaches, are based on Gaussian assumptions that are not applicable to multimodal random variables. The selection ensemble smoother is introduced as an alternative to traditional ensemble methods. In the proposed method, the prior distribution of the model variables, for example the porosity field, is a selection-Gaussian distribution, which allows modeling of the multimodal behavior of the posterior ensemble. The proposed approach is applied for validation on a two-dimensional synthetic channelized reservoir. In the application, an unknown reservoir model of porosity and permeability is estimated from the measured data. Seismic and production data are assumed to be repeatedly measured in time and the reservoir model is updated every time new data are assimilated. The example shows that the selection ensemble Kalman model improves the characterisation of the bimodality of the model parameters compared to the results of the ensemble smoother.

Improvements in understanding short-range permeability variability in coal seam gas reservoirs

2016 ◽  
Vol 56 (2) ◽  
pp. 555
Author(s):  
Stephen Tyson ◽  
Suzanne Hurter ◽  
Fengde Zhou ◽  
Morteza Jami
Keyword(s):  
Short Range ◽  
Simulation Models ◽  
Holistic Approach ◽  
Predictive Modelling ◽  
Gas Production ◽  
Production Performance ◽  
Improve Measurement ◽  
Production Wells ◽  
Production History ◽  
The Impact

After several years of production history on at least some of the more than 7,000 CSG production wells in the Surat and Bowen basins, reservoir engineers continue to note that understanding detailed permeability spatial variation near the well bore and its impact on actual production performance remains poor. There is a growing realisation that permeability of coals has an even higher variability than was initially expected, and that this variability occurs across a shorter range than that of the typical inter-well spacing (~750 m). As a result, flow between wells, pressure depletion, water and gas production rates and ultimate recovery is difficult to predict. Forecasting short-range continuity of different categories of absolute permeabilities through modelling is the key challenge. Other physical or geophysical parameters may change similarly with the same range. Generation models tend to over-estimate the lateral continuity of coals and associated carbonaceous shales resulting in a poor match between the model predictions and the observed production data. This may be due to incomplete information on the short-range variability of porosity and permeability and the appropriate up-scaled values for these parameters used in the reservoir simulation models. This extended abstract discusses controls on permeability, both the geological influences and the impact of drilling and completion on permeability. Taking a holistic approach to the problem of understanding permeability variability, the relative impact of these controls is estimated and discussed. With the benefit of rudimentary ranking of these controls, techniques have been developed to improve measurement and modelling of permeability variability. These approaches can help improve the predictive modelling capability of reservoir performance.

Intelligent Top-Down Reservoir Modeling of New Albany Shale

2009 ◽  
Author(s):  
Amirmasoud Kalantari Dahaghi ◽  
Shahab D. Mohaghegh
Keyword(s):  
Reservoir Modeling ◽  
Top Down ◽  
New Albany Shale

scholarly journals Impact of scale, nuclear PDF and temperature variations on the interpretation of medium-modified jet production data from the LHC

2021 ◽  
Vol 2021 (4) ◽  
Author(s):  
A. Andronic ◽  
J. Honermann ◽  
M. Klasen ◽  
C. Klein-Bösing ◽  
J. Salomon
Keyword(s):  
Distribution Functions ◽  
Production Data ◽  
Crossover Temperature ◽  
Parton Distribution Functions ◽  
Temperature Variations ◽  
Nuclear Modification Factor ◽  
Nuclear Modification ◽  
Modification Factor ◽  
The Impact ◽  
Jet Structure

Abstract In this paper we present a study of in-medium jet modifications performed with JEWEL and PYTHIA 6.4, focusing on the uncertainties related to variations of the perturbative scales and nuclear parton distribution functions (PDFs) and on the impact of the initial and crossover temperature variations of the medium. The simulations are compared to LHC data for the jet spectrum and the nuclear modification factor. We assess the interplay between the choice of nuclear PDFs and different medium parameters and study the impact of nuclear PDFs and the medium on the jet structure via the Lund plane.

scholarly journals Defining and Quantifying Intermittency in the Power Sector

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3366
Author(s):  
Daniel Suchet ◽  
Adrien Jeantet ◽  
Thomas Elghozi ◽  
Zacharie Jehl
Keyword(s):  
Power Source ◽  
Production Data ◽  
Power Sector ◽  
Predictive Capacity ◽  
The Public ◽  
Current State ◽  
Definition Of ◽  
Quantitative Definition ◽  
The Impact

The lack of a systematic definition of intermittency in the power sector blurs the use of this term in the public debate: the same power source can be described as stable or intermittent, depending on the standpoint of the authors. This work tackles a quantitative definition of intermittency adapted to the power sector, linked to the nature of the source, and not to the current state of the energy mix or the production predictive capacity. A quantitative indicator is devised, discussed and graphically depicted. A case study is illustrated by the analysis of the 2018 production data in France and then developed further to evaluate the impact of two methods often considered to reduce intermittency: aggregation and complementarity between wind and solar productions.

Sign in / Sign up

Export Citation Format

Share Document