Sequential coupling of thermal module into the reservoir simulator DMflow

2020 ◽  
pp. 103207
Author(s):  
Yongsheng Wang ◽  
Jun Li ◽  
Raheel Ahmed
Keyword(s):  
Sequential Coupling ◽  
Reservoir Simulator

Carbon Chain Growth by Sequential Reactions of CO and CO2 with a Transition Metal Carbonyl Complex

2018 ◽  
Author(s):  
Richard Kong ◽  
Mark Crimmin
Keyword(s):  
Transition Metal ◽  
Metal Carbonyl ◽  
Carbon Chain ◽  
Chain Growth ◽  
Carbonyl Complex ◽  
Fischer Tropsch ◽  
Carbon Chains ◽  
Sequential Coupling ◽  
Energy Economy ◽  
Sequential Reactions

<i>The formation of carbon chains by the coupling of COx (X = 1 or 2) units on transition metals is a fundamental step relevant to Fischer-Tropsch catalysis. Fischer-Tropsch catalysis produces energy dense liquid hydrocarbons from synthesis gas (CO and H2) and has been a mainstay of the energy economy since its discovery nearly a century ago. Despite detailed studies aimed at elucidating the steps of catalysis, experimental evidence for chain growth (Cn to Cn+1 ; n > 2) from the reaction of CO with metal complexes is unprecedented. In this paper, we show that carbon chains can be grown from sequential reactions of CO or CO2 with a transition metal carbonyl complex. By exploiting the cooperative effect of transition and main group metals, we document the first example of chain propagation from sequential coupling of CO units (C1 to C3 to C4), along with the first example of incorporation of CO2 into the growing carbon chain.</i><br>

Deployment of High-Resolution Reservoir Simulator: Methodology & Cases

2014 ◽  
Author(s):  
Alberto Cominelli ◽  
Claudio Casciano ◽  
Paola Panfili ◽  
Marco Rotondi ◽  
Paolo Del Bosco ◽  
...  
Keyword(s):  
High Resolution ◽  
Reservoir Simulator

A Method for Determining the Optimum Location of Wells in a Reservoir Using Mixed-Integer Programming

1974 ◽  
Vol 14 (01) ◽  
pp. 44-54 ◽  
Author(s):  
Gary W. Rosenwald ◽  
Don W. Green
Keyword(s):  
Mathematical Model ◽  
Gas Flow ◽  
Demand Curve ◽  
Gas Storage ◽  
Mixed Integer ◽  
Trial And Error ◽  
Flow Rates ◽  
Storage Reservoir ◽  
Reservoir Simulator ◽  
Production Demand

Abstract This paper presents a mathematical modeling procedure for determining the optimum locations of procedure for determining the optimum locations of wells in an underground reservoir. It is assumed that there is a specified production-demand vs time relationship for the reservoir under study. Several possible sites for new wells are also designated. possible sites for new wells are also designated. The well optimization technique will then select, from among those wellsites available, the locations of a specified number of wells and determine the proper sequencing of flow rates from Those wells so proper sequencing of flow rates from Those wells so that the difference between the production-demand curve and the flow curve actually attained is minimized. The method uses a branch-and-bound mixed-integer program (BBMIP) in conjunction with a mathematical reservoir model. The calculation with the BBMIP is dependent upon the application of superposition to the results from the mathematical reservoir model.This technique is applied to two different types of reservoirs. In the first, it is used for locating wells in a hypothetical groundwater system, which is described by a linear mathematical model. The second application of the method is to a nonlinear problem, a gas storage reservoir. A single-phase problem, a gas storage reservoir. A single-phase gas reservoir mathematical model is used for this purpose. Because of the nonlinearity of gas flow, purpose. Because of the nonlinearity of gas flow, superposition is not strictly applicable and the technique is only approximate. Introduction For many years, members of the petroleum industry and those concerned with groundwater hydrology have been developing mathematical reservoir modeling techniques. Through multiple runs of a reservoir simulator, various production schemes or development possibilities may be evaluated and their relative merits may be considered; i.e., reservoir simulators can be used to "optimize" reservoir development and production. Formal optimization techniques offer potential savings in the time and costs of making reservoir calculations compared with the generally used trial-and-error approach and, under proper conditions, can assure that the calculations will lead to a true optimum.This work is an extension of the application of models to the optimization of reservoir development. Given a reservoir, a designated production demand for the reservoir, and a number of possible sites for wells, the problem is to determine which of those sites would be the best locations for a specified number of new wells so that the production-demand curve is met as closely as possible. Normally, fewer wells are to be drilled than there are sites available. Thus, the question is, given n possible locations, at which of those locations should n wells be drilled, where n is less than n? A second problem, that of determining the optimum relative problem, that of determining the optimum relative flow rates of present and future wells is also considered. The problem is attacked through the simultaneous use of a reservoir simulator and a mixed-integer programming technique.There have been several reported studies concerned with be use of mathematical models to select new wells in gas storage or producing fields. Generally, the approach has been to use a trial-and-error method in which different well locations are assumed. A mathematical model is applied to simulate reservoir behavior under the different postulated conditions, and then the alternatives are postulated conditions, and then the alternatives are compared. Methods that evaluate every potential site have also been considered.Henderson et al. used a trial-and-error procedure with a mathematical model to locate new wells in an existing gas storage reservoir. At the same time they searched for the operational stratagem that would yield the desired withdrawal rates. In the reservoir that they studied, they found that the best results were obtained by locating new wells in the low-deliverability parts of the reservoir, attempting to maximize the distance between wells, and turning the wells on in groups, with the low-delivery wells turned on first.Coats suggested a multiple trial method for determining well locations for a producing field. SPEJ P. 44

Work Flow History Matches Numerical Simulation Models of Fractured Shale Wells

2021 ◽  
Vol 73 (04) ◽  
pp. 60-61
Author(s):  
Chris Carpenter
Keyword(s):  
Numerical Simulation ◽  
Fracture Toughness ◽  
Hydraulic Fracturing ◽  
History Matching ◽  
Simulation Models ◽  
Work Flow ◽  
Fracture Length ◽  
Matching Process ◽  
Rate Transient Analysis ◽  
Reservoir Simulator

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 199149, “Rate-Transient-Analysis-Assisted History Matching With a Combined Hydraulic Fracturing and Reservoir Simulator,” by Garrett Fowler, SPE, and Mark McClure, SPE, ResFrac, and Jeff Allen, Recoil Resources, prepared for the 2020 SPE Latin American and Caribbean Petroleum Engineering Conference, originally scheduled to be held in Bogota, Colombia, 17–19 March. The paper has not been peer reviewed. This paper presents a step-by-step work flow to facilitate history matching numerical simulation models of hydraulically fractured shale wells. Sensitivity analysis simulations are performed with a coupled hydraulic fracturing, geomechanics, and reservoir simulator. The results are used to develop what the authors term “motifs” that inform the history-matching process. Using intuition from these simulations, history matching can be expedited by changing matrix permeability, fracture conductivity, matrix-pressure-dependent permeability, boundary effects, and relative permeability. Introduction This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 199149, “Rate-Transient-Analysis-Assisted History Matching With a Combined Hydraulic Fracturing and Reservoir Simulator,” by Garrett Fowler, SPE, and Mark McClure, SPE, ResFrac, and Jeff Allen, Recoil Resources, prepared for the 2020 SPE Latin American and Caribbean Petroleum Engineering Conference, originally scheduled to be held in Bogota, Colombia, 17-19 March. The paper has not been peer reviewed. This paper presents a step-by-step work flow to facilitate history matching numerical simulation models of hydraulically fractured shale wells. Sensitivity analysis simulations are performed with a coupled hydraulic fracturing, geomechanics, and reservoir simulator. The results are used to develop what the authors term “motifs” that inform the history-matching process. Using intuition from these simulations, history matching can be expedited by changing matrix permeability, fracture conductivity, matrix-pressure-dependent permeability, boundary effects, and relative permeability. Introduction The concept of rate transient analysis (RTA) involves the use of rate and pressure trends of producing wells to estimate properties such as permeability and fracture surface area. While very useful, RTA is an analytical technique and has commensurate limitations. In the complete paper, different RTA motifs are generated using a simulator. Insights from these motif simulations are used to modify simulation parameters to expediate and inform the history- matching process. The simulation history-matching work flow presented includes the following steps: 1 - Set up a simulation model with geologic properties, wellbore and completion designs, and fracturing and production schedules 2 - Run an initial model 3 - Tune the fracture geometries (height and length) to heuristic data: microseismic, frac-hit data, distributed acoustic sensing, or other diagnostics 4 - Match instantaneous shut-in pressure (ISIP) and wellhead pressure (WHP) during injection 5 - Make RTA plots of the real and simulated production data 6 - Use the motifs presented in the paper to identify possible production mechanisms in the real data 7 - Adjust history-matching parameters in the simulation model based on the intuition gained from RTA of the real data 8 -Iterate Steps 5 through 7 to obtain a match in RTA trends 9 - Modify relative permeabilities as necessary to obtain correct oil, water, and gas proportions In this study, the authors used a commercial simulator that fully integrates hydraulic fracturing, wellbore, and reservoir simulation into a single modeling code. Matching Fracturing Data The complete paper focuses on matching production data, assisted by RTA, not specifically on the matching of fracturing data such as injection pressure and fracture geometry (Steps 3 and 4). Nevertheless, for completeness, these steps are very briefly summarized in this section. Effective fracture toughness is the most-important factor in determining fracture length. Field diagnostics suggest considerable variability in effective fracture toughness and fracture length. Typical half-lengths are between 500 and 2,000 ft. Laboratory-derived values of fracture toughness yield longer fractures (propagation of 2,000 ft or more from the wellbore). Significantly larger values of fracture toughness are needed to explain the shorter fracture length and higher net pressure values that are often observed. The authors use a scale- dependent fracture-toughness parameter to increase toughness as the fracture grows. This allows the simulator to match injection pressure data while simultaneously limiting fracture length. This scale-dependent toughness scaling parameter is the most-important parameter in determining fracture size.

Use of Horizontal Drift-Flux Models For Simulating Wellbore Flow in SAGD Operations

2021 ◽  
Author(s):  
Mohammad Heidari ◽  
Christopher Istchenko ◽  
William Bailey ◽  
Terry Stone
Keyword(s):  
Flux Flow ◽  
Pressure Drops ◽  
Steam Assisted Gravity Drainage ◽  
Drift Flux ◽  
Pressure Profiles ◽  
Well Model ◽  
Inclination Angles ◽  
Operation Pressure ◽  
Reservoir Simulator ◽  
High Degree

Abstract The paper examines new horizontal drift-flux correlations for their ability to accurately model phase flow rates and pressure drops in horizontal and undulating wells that are part of a Steam-Assisted Gravity Drainage (SAGD) field operation. Pressure profiles within each well correlate to the overall performance of the pair. SAGD is a low-pressure process that is sensitive to reservoir heterogeneity and other factors, hence accurate simulation of in situ wellbore pressures is critical for both mitigating uneven steam chamber evolution and optimizing wellbore design and operation. Recently published horizontal drift-flux correlations have been implemented in a commercial thermal reservoir simulator with a multi-segment well model. Valid for horizontally drilled wells with undulations, they complement previously reported drift-flux models developed for vertical and inclined wells down to approximately 5 degrees from horizontal. The formulation of these correlations has a high degree of nonlinearity. These models are tested in simulations of SAGD field operations. First, an overview of drift-flux models is discussed. This differentiates those based on vertical flow with gravity segregation to those that model horizontal flow with stratified and slug flow regimes. Second, the most recent and significant drift-flux correlation by Bailey et al. (2018, and hereafter referred to as Bailey-Tang-Stone) was robustly designed to be used in the well model of a reservoir simulator, can handle all inclination angles and was optimized to experimental data from the largest available databases to date. This and earlier drift-flux models are reviewed as to their strengths and weaknesses. Third, governing equations and implementation details are given of the Bailey-Tang-Stone model. Fourth, six case studies are presented that illustrate homogeneous and drift-flux flow model differences for various well scenarios.

A Proxy Flow Modelling Workflow to Estimate Gridded Dynamic Properties and Well Production Rates by Deep Learning Algorithms

2021 ◽  
Author(s):  
Soumi Chaki ◽  
Yevgeniy Zagayevskiy ◽  
Terry Wong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Dynamic Properties ◽  
Reservoir Management ◽  
Flow Modeling ◽  
Static Properties ◽  
Production Rates ◽  
Reservoir Properties ◽  
Well Production ◽  
Reservoir Simulator

Abstract This paper proposes a deep learning-based framework for proxy flow modeling to predict gridded dynamic petroleum reservoir properties (like pressure and saturation) and production rates for wells in a single framework. It approximates the solution of a full physics-based numerical reservoir simulator, but runs much more rapidly, allowing users to generate results for a much wider range of scenarios in a given time than could be done with a full physics simulator. The proxy can be used for reservoir management tasks like history matching, uncertainty quantification, and field development optimization. A deep-learning based methodology for accurate proxy-flow modeling is presented which combines U-Net (a variant of convolutional neural network) to predict gridded dynamic properties and deep neural network (DNN) models to forecast well production rates. First, gridded dynamic properties, such as reservoir pressure and phase saturations, are predicted from static properties like reservoir rock porosity and absolute permeability using a U-Net. Then, the static properties and the dynamic properties predicted by the U-Net are input to a DNN to predict production rates at the well perforations. The inclusion of U-net predicted pressure and saturations improves the quality of the well rate predictions. The proposed methodology is presented with the synthetic Brugge reservoir discretized into grid blocks. The U-Net input consists of three properties: dynamic gridded reservoir properties (such as pressure or fluid saturation) at the current state, static gridded porosity, and static gridded permeability. The U-Net has only one output property, the target gridded property (such as pressure or saturation) at the next time step. Training and testing datasets are generated by running 13 full physics flow simulations and dividing them in a 12:1 ratio. Nine U-Net models are calibrated to predict pressures/saturations, one for each of the nine grid layers present in the Brugge model. These outputs are then concatenated to obtain the complete pressure/saturation model for all nine layers. The constructed U-Net models match the distributions of generated pressures/saturations of the numerical reservoir simulator with a correlation coefficient value of approximately 0.99 and above 95% accuracy. The DNN models approximate well production rates accurately from U-Net predicted pressures and saturations along with static properties like transmissibility and horizontal permeability. For each well and each well perforation, the production rate is predicted with the DNN model. The use of the constructed proxy flow model generates reservoir predictions within a few minutes compared to the hours or days typically taken by a full physics flow simulator. The direct connection that is established between the gridded static and dynamic properties of the reservoir and well production rates using U-Net and DNN models has not been presented previously. Using only a small number of runs for its training, the workflow matches the numerical reservoir simulator results with reduced computational effort. This helps reservoir engineers make informed decisions more quickly, resulting in more efficient reservoir management.

Sign in / Sign up

Export Citation Format

Share Document