Modified CR-Type Material Balance Model for Well Production Forecasts in Case of Well Treatments

2021 ◽  
Author(s):  
Anna E. Gubanova ◽  
Bulat A. Khabibullin ◽  
Denis M. Orlov ◽  
Dmitry A. Koroteev
Keyword(s):  
Input Data ◽  
Historical Data ◽  
Material Balance ◽  
Optimization Procedure ◽  
Balance Model ◽  
Type Model ◽  
Hydrocarbon Production ◽  
Field Development ◽  
Oil Companies ◽  
Proxy Models

Abstract To reduce inefficient costs and environmental risks, oil companies strive to optimize the process of hydrocarbon production at all stages of field development, including geological and technical works at wells. In particular, it is important to predict fluid production with high accuracy. 3D hydrodynamic modeling is a generally accepted technique for solving this problem. It provides reliable results but requires many input data, computational resources, and time for calculations. Since the decision-making process has to be reactive, it is necessary to develop a simultaneously precise and prompt predictive instrument for quick forecasts of liquid production. The most promising tools for these purposes are proxy models based on solving the material balance equation. They adapt to the existing historical data even without PVT properties and reservoir data. Some of the most popular approaches are proxy models such as Capacitance Resistance Models (CRM). CR-type model is a material balance-based flow model, which provides preferable transmissibility trends, the presence of sealing or leaking faults with compressibility effects in consideration, and dissipation between injector-producer pairs. It is a data-driven model with adjustable time constants and interwell connectivity parameters. Before the model tuning, all parameters must be initialized with analytical or random approximations, and then they can be found by an appropriate optimization procedure. Historical-based Capacitance Models can be applied to poorly studied fields. Besides, they give an opportunity to rapidly optimize field development strategy by making calculations with different well exploitation parameters. They only require historical data of hydrocarbon production volumes, injection profiles, and bottom-hole pressure dynamics as input data. One of the main is that properties in the interwell space are estimated approximately and considered to be constant throughout the entire development history. However, this is a weak assumption in the case of including well interventions and stimulations. Thus, the main goal of this work is to adjust coefficients online to changes in well operation modes, introducing new wells or shut-in the existing ones. Since the governing equation includes the considered CRM improvement, users can perform optimization over different timespans, including "special" intervals. As a result, weighting connectivity parameters of the model can be depicted on a map of well interactions versus time.

scholarly journals Hierarchical Approach to Identifying Fluid Flow Models in a Heterogeneous Porous Medium

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3289
Author(s):  
Emil N. Musakaev ◽  
Sergey P. Rodionov ◽  
Nail G. Musakaev
Keyword(s):  
Measurement Errors ◽  
History Matching ◽  
Input Data ◽  
Oil And Gas ◽  
Material Balance ◽  
Oil Field ◽  
Balance Model ◽  
Successive Approximations ◽  
Hierarchical Approach ◽  
Matching Problem

A three-dimensional numerical hydrodynamic model fairly accurately describes the processes of developing oil and gas fields, and has good predictive properties only if there are high-quality input data and comprehensive information about the reservoir. However, under conditions of high uncertainty of the input data, measurement errors, significant time and resource costs for processing and analyzing large amounts of data, the use of such models may be unreasonable and can lead to ill-posed problems: either the uniqueness of the solution or its stability is violated. A well-known method for dealing with these problems is regularization or the method of adding some additional a priori information. In contrast to full-scale modeling, currently there is active development of reduced-physics models, which are used, first of all, in conditions when it is required to make an operational decision, and computational resources are limited. One of the most popular simplified models is the material balance model, which makes it possible to directly capture the relationship between reservoir pressure, flow rates and the integral reservoir characteristics. In this paper, it is proposed to consider a hierarchical approach when solving the problem of oil field waterflooding control using material balance models in successive approximations: first for the field as a whole, then for hydrodynamically connected blocks of the field, then for wells. When moving from one level of model detailing to the next, the modeling results from the previous levels of the hierarchy are used in the form of additional regularizing information, which ultimately makes it possible to correctly solve the history matching problem (identification of the filtration model) in conditions of incomplete input information.

Forecasting Hydrocarbon Production at Gas Condensate Fields Considering Phase Transformations of Reservoir Systems

2021 ◽  
Author(s):  
Yefim Semenovich Bikman
Keyword(s):  
Phase Transformations ◽  
Material Balance ◽  
Weighted Average ◽  
Gas Condensate ◽  
Reservoir Pressure ◽  
Hydrocarbon Production ◽  
Hydrocarbon Recovery ◽  
Field Development ◽  
Phase Balance ◽  
Gas Drive

Abstract Based on the results of PVT studies, a methodology for estimating hydrocarbon recovery at various stages of a gas condensate field development, depending on the current weighted average reservoir pressure in the gas drive, is considered. In this case, the physical processes related to the phase transformations of the reservoir gas condensate mixture with a decrease in reservoir pressure in the deposit are assumed identical in the PVT bomb. That is, the effect of the porous medium is neglected. This allows describing the processes of phase transformations with the same equation of material balance, based on which it is possible to forecast hydrocarbon recovery at gas condensate fields, and provide a control over the results of phase transformation modelling of the reservoir gas condensate mixture in phase balance bomb (PVT bomb).

scholarly journals Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1055
Author(s):  
Qian Sun ◽  
William Ampomah ◽  
Junyu You ◽  
Martha Cather ◽  
Robert Balch
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
History Matching ◽  
Optimization Problems ◽  
Learning Technologies ◽  
Petroleum Engineering ◽  
Support Vector ◽  
Learning Approaches ◽  
Field Development ◽  
Proxy Models

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

Integrated Production Model as a Tool for Optimization the Development Strategy of the Sakhalin Oil and Gas Condensate Field

2021 ◽  
Author(s):  
Oleksandr Doroshenko ◽  
Miljenko Cimic ◽  
Nicholas Singh ◽  
Yevhen Machuzhak
Keyword(s):  
History Matching ◽  
Effective Field ◽  
Production Optimization ◽  
Production Model ◽  
Production Forecast ◽  
Hydrocarbon Production ◽  
Integrated Production ◽  
Wellhead Pressure ◽  
Field Development ◽  
The Impact

Abstract A fully integrated production model (IPM) has been implemented in the Sakhalin field to optimize hydrocarbons production and carried out effective field development. To achieve our goal in optimizing production, a strategy has been accurately executed to align the surface facilities upgrade with the production forecast. The main challenges to achieving the goal, that we have faced were:All facilities were designed for early production stage in late 1980's, and as the asset outdated the pipeline sizes, routing and compression strategies needs review.Detecting, predicting and reducing liquid loading is required so that the operator can proactively control the hydrocarbon production process.No integrated asset model exists to date. The most significant engineering tasks were solved by creating models of reservoirs, wells and surface network facility, and after history matching and connecting all the elements of the model into a single environment, it has been used for the different production forecast scenarios, taking into account the impact of infrastructure bottlenecks on production of each well. This paper describes in detail methodology applied to calculate optimal well control, wellhead pressure, pressure at the inlet of the booster compressor, as well as for improving surface flowlines capacity. Using the model, we determined the compressor capacity required for the next more than ten years and assessed the impact of pipeline upgrades on oil gas and condensate production. Using optimization algorithms, a realistic scenario was set and used as a basis for maximizing hydrocarbon production. Integrated production model (IPM) and production optimization provided to us several development scenarios to achieve target production at the lowest cost by eliminating infrastructure constraints.

scholarly journals A statistical mass-balance model for reconstruction of LIA ice mass for glaciers in the European Alps

2007 ◽  
Vol 46 ◽  
pp. 161-169 ◽  
Author(s):  
Wolfgang Schöner ◽  
Reinhard Böhm
Keyword(s):  
Mass Balance ◽  
Input Data ◽  
Driving Forces ◽  
Winter Precipitation ◽  
Mean Value ◽  
Balance Model ◽  
European Alps ◽  
Seasonal Temperature ◽  
Mass Balance Model ◽  
Linear Regression Models

AbstractStepwise linear regression models were calibrated against the measured mass balance of glaciers in the Austrian Alps for the prediction of specific annual net balance and summer balance from climatological and topographical input data. For estimation of winter mass balance, a simple ratio between the amount of winter precipitation and the measured winter balance was used. A ratio with a mean value of 2.0 and a standard deviation of 0.44 was derived from the sample of measured winter balances. Climate input data were taken from the HISTALP database which offers a homogenized data source that is outstanding in terms of its spatial and temporal coverage. Data from the Austrian glacier inventory were used as topographical input data. From the group of possible predictors summer air temperature, winter precipitation, summer snow precipitation and continentality (as defined from seasonal temperature variation) were selected as climatological driving forces in addition to lowest glacier elevation and area-weighted mean glacier elevation as topographical driving forces. Summer temperature explains 60% of the variance of summer mass balance and 39% of the variance of annual mass balance. Additional factors increase the explained variance by 22% for summer and 31% for annual net balance. The calibrated mass-balance model was used to reconstruct the mass balance of Hintereisferner and Vernagtferner back to 1800. Whereas the model performs well for Hintereisferner, it fails for some sub-periods for Vernagtferner due to the complicated flow dynamics of the glacier.

Study on the Methods of Layer Adjustment in Gas Dynamic Reserve Measurement

2013 ◽  
Vol 671-674 ◽  
pp. 142-145
Author(s):  
Yi Zhang ◽  
Bang Hua Liu ◽  
Qing Min Gan ◽  
Hai Xia Shi ◽  
Jun Feng Liu
Keyword(s):  
Material Balance ◽  
Oil Field ◽  
Linear Extrapolation ◽  
Field Development ◽  
Oil Field Development ◽  
Basic Work ◽  
Model Condition ◽  
Before And After ◽  
The One ◽  
Straight Lines

To get the accurate gas pool dynamic measurement is the one of the basic work of oil field development. The geologic conditions, one of the aspects, limited the gas pool. It often appears reshooting another layer to commingled production or block off the seriously water producer in layer adjustment, calculation of reserves depends on the alteration of the model condition. Through the material balance and its further work, set the gas pool reserves calculation methods under the layer adjustment condition. The closed constant volume gas pool, its drawdown curve becomes the transition with the adjustment of the layer. Through the original formation pressure with two different slope straight lines before and after adjustment, Using linear extrapolation can get the reserves before and after adjusted.

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