Optimization of Waterflooding Utilizing Data Driven Models: An Application of the Two-Phase Capacitance Resistance Model

2021 ◽  
Author(s):  
Ahmed Alghamdi Abdullah Ghamdi ◽  
Daniel Opoku ◽  
Abeeb Awotunde ◽  
Mohamed Mahmoud ◽  
Qinzhuo Liao
Keyword(s):  
Time Constant ◽  
Pore Volume ◽  
Injection Rate ◽  
Data Driven ◽  
Production Data ◽  
Model Parameters ◽  
Two Phase ◽  
Reservoir Characteristics ◽  
Resistance Model ◽  
Injection Rates

Abstract The Capacitance-Resistance Model, commonly known as CRM, is a data-driven model derived from the material balance equation, and only requires production and injection data for history matching and prediction of reservoir performance. The CRM has two model parameters: The input and output are related the first parameter is the connectivity (also called gain, or weight), which is a dimensionless number that quantifies the connectivity between producers and injectors (i.e. how much of the input is supporting the output). The second parameter is the time delay (also called time constant) and is a function of pore volume, total compressibility, and productivity indices, and it represents the time it takes for the input (injection) to result in an output (production). Since the CRM inception in 2005, several authors have further developed it to increase its range of applications. When CRM was first introduced, it was suited most for single-phase reservoirs. A recent improvement of the CRM added two-phase capability. In this project, Two-phase CRM was utilized to test how this tool performed in waterflooding optimization. The main hypothesis in CRM is that the several reservoir characteristics can be inferred from analyzing production and injection data only. These reservoir characteristics are the connectivity, which can be thought of as an analog to permeability, and the time constant, which is a measure of the pore volume and compressibility. CRM does not require core data, logs, seismic, or any rock or fluids properties. This hypothesis, that reservoir characteristics can be inferred from injection and production data, can be challenged easily since most reservoirs have gradients of fluid properties, multi-porosity systems, and heterogeneous formations with different wettability presences. Regardless, several publications have shown that CRM can result in high certainty output. To test the two-phase CRM, three synthetic heterogeneous reservoirs were created. Model 1 was developed with nearly stabilized injection and production data. Model 2 had more fluctuations in the injection data than model 1. And model 3 had extreme fluctuations in injection data compared to model 2 with lower rock and fluid compressibilities. The results presented in this project show that the CRM ability to match field production depends largely on two aspects: first is the compressibility of the system. When the compressibility was lowered in model 3, the CRM achieved excellent results. The second aspect is the degree of the fluctuations in injection rate the CRM is developed upon. Model 2 with a higher degree of injection rate fluctuations than model 1 has achieved a better future prediction performance. CRM model 3 was used to optimize the field waterflooding injection rates subject to two constraints, The first constraint is a set value for maximum field injection rate at any time step while the second constraint limits each injector maximum injection rate. The optimization of the annual injection rates has added 290,000 bbls of oil produced.

Data-Driven Prediction of Unconventional Shale-Reservoir Dynamics

SPE Journal ◽  
2020 ◽  
Vol 25 (05) ◽  
pp. 2564-2581 ◽  
Author(s):  
Hector Klie ◽  
Horacio Florez
Keyword(s):  
Linear Operators ◽  
Dynamic Mode Decomposition ◽  
Data Driven ◽  
Production Data ◽  
Dynamic Mode ◽  
State Variables ◽  
Coupled Flow ◽  
Two Phase ◽  
Data Set ◽  
Well Production

Summary The present work introduces extended dynamic mode decomposition (EDMD) as a suitable data-driven framework for learning the reservoir dynamics entailed by flow/fracture interactions in unconventional shales. The proposed EDMD approach builds on the approximation of infinite-dimensional linear operators combined with the power of deep learning autoencoder networks to extract salient transient features from pressure/stress fields and bulks of production data. The data-driven model is demonstrated on three illustrative examples involving single- and two-phase coupled flow/geomechanics simulations and a real production data set from the Vaca Muerta unconventional shale formation in Argentina. We demonstrated that we could attain a high level of predictability from unseen field-state variables and well-production data given relatively moderate input requirements. As the main conclusion of this work, EDMD stands as a promising data-driven choice for efficiently reconstructing flow/fracture dynamics that are either partially or entirely unknown, or that are too complex to formulate using known simulation tools on unconventional plays.

State-of-the-Art Solution of Capacitance Resistance Model by Considering Dynamic Time Constants as a Realistic Assumption

2017 ◽  
Vol 140 (1) ◽  
Author(s):  
A. Lesan ◽  
S. Ehsan Eshraghi ◽  
A. Bahroudi ◽  
M. Reza Rasaei ◽  
H. Rahami
Keyword(s):  
Time Constant ◽  
History Matching ◽  
Injection Rate ◽  
Water Flooding ◽  
Production Flow ◽  
Time Constants ◽  
New Model ◽  
Resistance Model ◽  
Model Dependency ◽  
Dynamic Time

To have an acceptable accuracy for water flooding projects, proper history matching is an important tool. Capacitance resistance model (CRM) simulates water flooding performance based on two tuning parameters of time constant and connectivity. Main advantages of CRM are its simplicity and fastness; furthermore, it needs only some field-available inputs like injection and production flow rates. CRM is reliable if producers receive the injection rate signal; in other words, duration of history matching must be enough so that the rate signal of injection is sensed in producers. It is a shortcoming of CRM that the results might not be accurate as a result of short history. In the common CRM, time constant is considered to be a static parameter (constant number) during the history of simulation. However, time constant is a time-dependent function that depends on the reservoir nature. In this paper, a new model has been developed as it decreases model dependency on the history matching length by shifting time axis. This new definition adds a rate shift constant to the model mathematics. Moreover, a new model is considering dynamic time constants. This new model is called dynamic capacitance resistance model (DCRM). Two reservoir models have been simulated to analyze the performance of DCRM, and, as a result, it is found that the static time constant is an erroneous assumption. Finally, the accuracy of the results has been improved since the degree-of-freedom of the CRM increased in the new version.

Prediction of flow rates and stability in large scale airlift reactors

1996 ◽  
Vol 34 (5-6) ◽  
pp. 51-57 ◽  
Author(s):  
John W. Hinks ◽  
Howard Cawte ◽  
David A. Sanders ◽  
Adam Hudson ◽  
Christopher N. Dockree
Keyword(s):  
Large Scale ◽  
Injection Rate ◽  
Air Injection ◽  
Stable Operation ◽  
Two Phase ◽  
Nineteen Seventies ◽  
Effluent Quality ◽  
Airlift Reactors ◽  
Injection Rates

Large-scale High Recirculation Airlift Reactors have been used to treat biodegradable waste waters since the mid nineteen seventies. The system is particularly attractive for situations where the land to locate wastewater works is restricted. Little is known, however, about the fluid dynamics of the gas-liquid mixture flowing around the reactor. This makes the determination of air injection rates difficult if effluent quality and dynamic stability are to be maintained. When the air injected is not sufficient to maintain stable operation the reactor contents may reverse violently resulting in down time, failure to achieve target discharge quality and possible damage to the reactor itself. As a result many reactor installations operate at air injection rates above those necessary for the biological processes. The extra air injected results in higher capital and process costs. This paper considers the effect of air injection rates on the hydrodynamic stability of Airlift Reactors and a two-phase model is proposed to predict stable operation at a reduced air injection rate. Results are presented which show the effect of reactor design on stability.

CALCULATION OF OPTIMAL INJECTION RATE FOR DISPERSED WATERFLOODING SYSTEM

2017 ◽  
pp. 63-67
Author(s):  
L. A. Vaganov ◽  
A. Yu. Sencov ◽  
A. A. Ankudinov ◽  
N. S. Polyakova
Keyword(s):  
Injection Rate ◽  
Oil Field ◽  
Injection Well ◽  
Water Migration ◽  
Mutual Location ◽  
Optimal Injection ◽  
Technical Measures ◽  
Injection Rates

The article presents a description of the settlement method of necessary injection rates calculation, which is depended on the injected water migration into the surrounding wells and their mutual location. On the basis of the settlement method the targeted program of geological and technical measures for regulating the work of the injection well stock was created and implemented by the example of the BV7 formation of the Uzhno-Vyintoiskoe oil field.

Simulation of mechanically stirred two-phase liquid-gas flow

1986 ◽  
Vol 51 (5) ◽  
pp. 1001-1015 ◽  
Author(s):  
Ivan Fořt ◽  
Vladimír Rogalewicz ◽  
Miroslav Richter
Keyword(s):  
Spatial Distribution ◽  
Velocity Field ◽  
Gas Flow ◽  
Liquid Velocity ◽  
Model Parameters ◽  
Two Phase ◽  
Mean Velocity ◽  
Rushton Turbine ◽  
Low Viscosity ◽  
Volumetric Gas Flow Rate

The study describes simulation of the motion of bubbles in gas, dispersed by a mechanical impeller in a turbulent low-viscosity liquid flow. The model employs the Monte Carlo method and it is based both on the knowledge of the mean velocity field of mixed liquid (mean motion) and of the spatial distribution of turbulence intensity ( fluctuating motion) in the investigated system - a cylindrical tank with radial baffles at the wall and with a standard (Rushton) turbine impeller in the vessel axis. Motion of the liquid is then superimposed with that of the bubbles in a still environment (ascending motion). The computation of the simulation includes determination of the spatial distribution of the gas holds-up (volumetric concentrations) in the agitated charge as well as of the total gas hold-up system depending on the impeller size and its frequency of revolutions, on the volumetric gas flow rate and the physical properties of gas and liquid. As model parameters, both liquid velocity field and normal gas bubbles distribution characteristics are considered, assuming that the bubbles in the system do not coalesce.

scholarly journals Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4836
Author(s):  
Liping Zhang ◽  
Yifan Hu ◽  
Qiuhua Tang ◽  
Jie Li ◽  
Zhixiong Li
Keyword(s):  
Decision Making ◽  
Data Base ◽  
Real Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Data Driven ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Production Data ◽  
Online Decision Making

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.

Analytical Rate-Transient Analysis and Production Performance of Waterflooded Fields with Delayed Injection Support

2021 ◽  
pp. 1-23
Author(s):  
Daniel O'Reilly ◽  
Manouchehr Haghighi ◽  
Mohammad Sayyafzadeh ◽  
Matthew Flett
Keyword(s):  
Steady State ◽  
Transient Analysis ◽  
Water Injection ◽  
Data Interpretation ◽  
Production Performance ◽  
Production Data ◽  
Reservoir Properties ◽  
Two Phase ◽  
Production Forecasting ◽  
Rate Transient Analysis

Summary An approach to the analysis of production data from waterflooded oil fields is proposed in this paper. The method builds on the established techniques of rate-transient analysis (RTA) and extends the analysis period to include the transient- and steady-state effects caused by a water-injection well. This includes the initial rate transient during primary production, the depletion period of boundary-dominated flow (BDF), a transient period after injection starts and diffuses across the reservoir, and the steady-state production that follows. RTA will be applied to immiscible displacement using a graph that can be used to ascertain reservoir properties and evaluate performance aspects of the waterflood. The developed solutions can also be used for accurate and rapid forecasting of all production transience and boundary-dominated behavior at all stages of field life. Rigorous solutions are derived for the transient unit mobility displacement of a reservoir fluid, and for both constant-rate-injection and constant-pressure-injection after a period of reservoir depletion. A simple treatment of two-phase flow is given to extend this to the water/oil-displacement problem. The solutions are analytical and are validated using reservoir simulation and applied to field cases. Individual wells or total fields can be studied with this technique; several examples of both will be given. Practical cases are given for use of the new theory. The equations can be applied to production-data interpretation, production forecasting, injection-water allocation, and for the diagnosis of waterflood-performanceproblems. Correction Note: The y-axis of Fig. 8d was corrected to "Dimensionless Decline Rate Integral, qDdi". No other content was changed.

Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

2018 ◽  
Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Measurement Data ◽  
Data Driven ◽  
Sensor Data ◽  
Model Parameters ◽  
High Fidelity ◽  
System Behavior ◽  
Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

scholarly journals Constitutive Modelling and Identification of Parameters of 316L Stainless Steel at Cryogenic Temperatures

2014 ◽  
Vol 8 (3) ◽  
pp. 136-140 ◽  
Author(s):  
Maciej Ryś
Keyword(s):  
Phase Transformation ◽  
Nonlinear Response ◽  
316L Stainless Steel ◽  
Parent Phase ◽  
Secondary Phase ◽  
Material Model ◽  
Transformation Process ◽  
Constitutive Modelling ◽  
Model Parameters ◽  
Two Phase

Abstract In this work, a macroscopic material model for simulation two distinct dissipative phenomena taking place in FCC metals and alloys at low temperatures: plasticity and phase transformation, is presented. Plastic yielding is the main phenomenon occurring when the yield stress is reached, resulting in nonlinear response of the material during loading. The phase transformation process leads to creation of two-phase continuum, where the parent phase coexists with the inclusions of secondary phase. An identification of the model parameters, based on uniaxial tension test at very low temperature, is also proposed.

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