scholarly journals Numerical Study on the Effects of Imbibition on Gas Production and Shut-In Time Optimization in Woodford Shale Formation

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3222 ◽  
Author(s):  
Zhou Zhou ◽  
Shiming Wei ◽  
Rong Lu ◽  
Xiaopeng Li
Keyword(s):  
Numerical Model ◽  
Hydraulic Fracturing ◽  
Field Data ◽  
Gas Production ◽  
Model Parameters ◽  
Water Recovery ◽  
Woodford Shale ◽  
Capillary Suction ◽  
Shale Formation ◽  
The Relationship

In shale gas formations, imbibition is significant since the tight pore structure causes a strong capillary suction pressure. After hydraulic fracturing, imbibition during the period of shut-in affects the water recovery of flowback. Although there have been many studies investigating imbibition in shale formations, few papers have studied the relationship between gas production and shut-in time under the influence of imbibition. This paper developed a numerical model to investigate the effect of imbibition on gas production to optimize the shut-in time after hydraulic fracturing. This numerical model is a 2-D two-phase (gas and water) imbibition model for simulating an imbibed fluid flow and its effect on permeability, flowback, and water recovery. The experimental and field data from the Woodford shale formation were matched by the model to properly configure and calibrate the model parameters. The experimental data consisted of the relationship between the imbibed fluid volume and permeability change, the relative permeability, and the capillary pressure for the Woodford shale samples. The Woodford field data included the gas production and flowback volume. The modeling results indicate that imbibition can be a beneficial factor for gas production, since it can increase rock permeability. However, the gas production would be reduced when excessive fluid is imbibed by the shale matrix. Therefore, the shut-in time after hydraulic fracturing, when the imbibition happens in shale, could be optimized to maximize the gas production.

Multiphase Transient Slugging Flow in Subsea Oil and Gas Production

2016 ◽  
Author(s):  
Zhenhua Zhang ◽  
Longbin Tao
Keyword(s):  
Data Analysis ◽  
Temperature Distribution ◽  
Numerical Model ◽  
Slug Flow ◽  
Field Data ◽  
Oil And Gas ◽  
Gas Production ◽  
Worst Case ◽  
Oil And Gas Production ◽  
Significant Difference

Slug flow in horizontal pipelines and riser systems in deep sea has been proved as one of the challenging flow assurance issues. Large and fluctuating gas/liquid rates can severely reduce production and, in the worst case, shut down, depressurization or damage topside equipment, such as separator, vessels and compressors. Previous studies are primarily based on experimental investigations of fluid properties with air/water as working media in considerably scaled down model pipes, and the results cannot be simply extrapolated to full scale due to the significant difference in Reynolds number and other fluid conditions. In this paper, the focus is on utilizing practical shape of pipe, working conditions and fluid data for simulation and data analysis. The study aims to investigate the transient multiphase slug flow in subsea oil and gas production based on the field data, using numerical model developed by simulator OLGA and data analysis. As the first step, cases with field data have been modelled using OLGA and validated by comparing with the results obtained using PIPESYS in steady state analysis. Then, a numerical model to predict slugging flow characteristics under transient state in pipeline and riser system was set up using multiphase flow simulator OLGA. One of the highlights of the present study is the new transient model developed by OLGA with an added capacity of newly developed thermal model programmed with MATLAB in order to represent the large variable temperature distribution of the riser in deep water condition. The slug characteristics in pipelines and temperature distribution of riser are analyzed under the different temperature gradients along the water depth. Finally, the depressurization during a shut-down and then restart procedure considering hydrate formation checking is simulated. Furthermore, slug length, pressure drop and liquid hold up in the riser are predicted under the realistic field development scenarios.

Koala habitat use and population density: using field data to test the assumptions of ecological models

2013 ◽  
Vol 35 (2) ◽  
pp. 160 ◽  
Author(s):  
William Ellis ◽  
Sean FitzGibbon ◽  
Alistair Melzer ◽  
Robbie Wilson ◽  
Steve Johnston ◽  
...  
Keyword(s):  
Field Data ◽  
Model Parameters ◽  
Diet Choice ◽  
Limited Data ◽  
Phascolarctos Cinereus ◽  
The Relationship ◽  
Term Data ◽  
Conservation Decisions ◽  
Conservation And Management

In principle, conservation planning relies on long-term data; in reality, conservation decisions are apt to be based upon limited data and short-range goals. For the koala (Phascolarctos cinereus), frequently reliance is made on the assumption that indirect signs can be used to indicate behavioural preferences, such as diet choice. We examined the relationship between the use of trees by koalas and the presence of scats beneath those trees. Tree use was associated with scat presence on 49% of occasions when koalas were radio-tracked in both central Queensland (n = 10 koalas) and south-east Queensland (n = 5 koalas), increasing to 77% of occasions when trees were rechecked the following day. Koala densities were correlated with scat abundance at sites with koala density between ~0.2 and 0.6 koalas per hectare. Our results confirm that scat searches are imprecise indicators of tree use by koalas, but demonstrate that these searches can be used, with caveats, to estimate koala population densities. We discuss how errors in estimating or applying predictive model parameters can bias estimates of occupancy and show how a failure to validate adequately the assumptions used in modelling and mapping can undermine the power of the products to direct rational conservation and management efforts.

Productivity Prediction of Coalbed Methane Considering the Permeability Changes in Coal

2014 ◽  
Author(s):  
H.. Chen ◽  
M.. Li ◽  
Y.. Zhang ◽  
C.. Liu ◽  
Y.. Li
Keyword(s):  
Numerical Model ◽  
Coalbed Methane ◽  
Field Data ◽  
Gas Production ◽  
Phase Flow ◽  
Two Phase ◽  
Gas Desorption ◽  
Permeability Changes ◽  
Cbm Production ◽  
Production Period

Abstract This paper describes a three-dimensional numerical model for predicting the coalbed methane (CBM) production. The model describes single phase gas desorption from coal matrix, diffusion to the fracture and two-phase flow of gas and water in the natural fracture system as well as the permeability changes in coal which result from effective stress changes and matrix shrinkage due to gas desorption. The model was discretized by a finite difference method. The implicit pressure-explicit saturation (IMPES) method was used to solve the two-phase flow equations and gas desorption equation was solved implicitly. The numerical model was validated by the field data from Qinshui basin in China. Based on the model, the impact of various reservoir and Langmuir isothermal adsorption parameters on the gas production was investigated. The results show that the gas production rate of the coalbed methane predicted by this model is in good accordance with the field data. The permeability near the wellbore dramatically decreases as the reservoir pressure drops at the early production period while at the later production period, the permeability near the wellbore increases because of the matrix shrinkage. The permeability changes far away from the wellbore are not so remarkable. In addition, the gas production rate increases with the increased permeability, seam thickness and Langmuir pressure constant while it decreases with the increased porosity and Langmuir volume constant. The numerical model can be used to predict and analyze the production performance of CBM reservoirs and the research results provide theoretical support for CBM production.

Simulation of proppant transport and fracture plugging in the framework of a radial hydraulic fracturing model

2020 ◽  
Vol 35 (6) ◽  
pp. 325-339
Author(s):  
Vasily N. Lapin ◽  
Denis V. Esipov
Keyword(s):  
Numerical Model ◽  
Hydraulic Fracturing ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Injection Rate ◽  
Fluid Injection ◽  
Subtraction Technique ◽  
Gas Industry ◽  
Proppant Transport ◽  
Hydraulic Fracture Propagation

AbstractHydraulic fracturing technology is widely used in the oil and gas industry. A part of the technology consists in injecting a mixture of proppant and fluid into the fracture. Proppant significantly increases the viscosity of the injected mixture and can cause plugging of the fracture. In this paper we propose a numerical model of hydraulic fracture propagation within the framework of the radial geometry taking into account the proppant transport and possible plugging. The finite difference method and the singularity subtraction technique near the fracture tip are used in the numerical model. Based on the simulation results it was found that depending on the parameters of the rock, fluid, and fluid injection rate, the plugging can be caused by two reasons. A parameter was introduced to separate these two cases. If this parameter is large enough, then the plugging occurs due to reaching the maximum possible concentration of proppant far from the fracture tip. If its value is small, then the plugging is caused by the proppant reaching a narrow part of the fracture near its tip. The numerical experiments give an estimate of the radius of the filled with proppant part of the fracture for various injection rates and leakages into the rock.

scholarly journals New Hybrid Approach for Developing Automated Machine Learning Workflows: A Real Case Application in Evaluation of Marcellus Shale Gas Production

Fuels ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 286-303
Author(s):  
Vuong Van Pham ◽  
Ebrahim Fathi ◽  
Fatemeh Belyadi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Shale Gas ◽  
Field Data ◽  
Marcellus Shale ◽  
Hybrid Approach ◽  
Gas Production ◽  
Bayesian Optimization ◽  
Real Field ◽  
Engineering Problems

The success of machine learning (ML) techniques implemented in different industries heavily rely on operator expertise and domain knowledge, which is used in manually choosing an algorithm and setting up the specific algorithm parameters for a problem. Due to the manual nature of model selection and parameter tuning, it is impossible to quantify or evaluate the quality of this manual process, which in turn limits the ability to perform comparison studies between different algorithms. In this study, we propose a new hybrid approach for developing machine learning workflows to help automated algorithm selection and hyperparameter optimization. The proposed approach provides a robust, reproducible, and unbiased workflow that can be quantified and validated using different scoring metrics. We have used the most common workflows implemented in the application of artificial intelligence (AI) and ML in engineering problems including grid/random search, Bayesian search and optimization, genetic programming, and compared that with our new hybrid approach that includes the integration of Tree-based Pipeline Optimization Tool (TPOT) and Bayesian optimization. The performance of each workflow is quantified using different scoring metrics such as Pearson correlation (i.e., R2 correlation) and Mean Square Error (i.e., MSE). For this purpose, actual field data obtained from 1567 gas wells in Marcellus Shale, with 121 features from reservoir, drilling, completion, stimulation, and operation is tested using different proposed workflows. A proposed new hybrid workflow is then used to evaluate the type well used for evaluation of Marcellus shale gas production. In conclusion, our automated hybrid approach showed significant improvement in comparison to other proposed workflows using both scoring matrices. The new hybrid approach provides a practical tool that supports the automated model and hyperparameter selection, which is tested using real field data that can be implemented in solving different engineering problems using artificial intelligence and machine learning. The new hybrid model is tested in a real field and compared with conventional type wells developed by field engineers. It is found that the type well of the field is very close to P50 predictions of the field, which shows great success in the completion design of the field performed by field engineers. It also shows that the field average production could have been improved by 8% if shorter cluster spacing and higher proppant loading per cluster were used during the frac jobs.

scholarly journals Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Satoshi Miyamoto ◽  
Zu Soh ◽  
Shigeyuki Okahara ◽  
Akira Furui ◽  
Taiichi Takasaki ◽  
...  
Keyword(s):  
Cardiopulmonary Bypass ◽  
Flow Rate ◽  
Neurological Complications ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Perfusion Flow ◽  
Postsurgical Complication ◽  
Tenfold Cross Validation ◽  
Suction Flow ◽  
The Relationship

AbstractThe need for the estimation of the number of microbubbles (MBs) in cardiopulmonary bypass surgery has been recognized among surgeons to avoid postoperative neurological complications. MBs that exceed the diameter of human capillaries may cause endothelial disruption as well as microvascular obstructions that block posterior capillary blood flow. In this paper, we analyzed the relationship between the number of microbubbles generated and four circulation factors, i.e., intraoperative suction flow rate, venous reservoir level, continuous blood viscosity and perfusion flow rate in cardiopulmonary bypass, and proposed a neural-networked model to estimate the number of microbubbles with the factors. Model parameters were determined in a machine-learning manner using experimental data with bovine blood as the perfusate. The estimation accuracy of the model, assessed by tenfold cross-validation, demonstrated that the number of MBs can be estimated with a determinant coefficient R2 = 0.9328 (p < 0.001). A significant increase in the residual error was found when each of four factors was excluded from the contributory variables. The study demonstrated the importance of four circulation factors in the prediction of the number of MBs and its capacity to eliminate potential postsurgical complication risks.

scholarly journals Influence of Selected Model Parameters on the Electromagnetic Levitation Melting Efficiency

2021 ◽  
Vol 11 (9) ◽  
pp. 3827
Author(s):  
Blazej Nycz ◽  
Lukasz Malinski ◽  
Roman Przylucki
Keyword(s):  
Electromagnetic Levitation ◽  
Optimization Methods ◽  
Calculation Model ◽  
Model Parameters ◽  
Starting Point ◽  
Low Efficiency ◽  
The Relationship ◽  
Reactive Metals ◽  
Electromagnetic Levitation Melting ◽  
Metal Melting

The article presents the results of multivariate calculations for the levitation metal melting system. The research had two main goals. The first goal of the multivariate calculations was to find the relationship between the basic electrical and geometric parameters of the selected calculation model and the maximum electromagnetic buoyancy force and the maximum power dissipated in the charge. The second goal was to find quasi-optimal conditions for levitation. The choice of the model with the highest melting efficiency is very important because electromagnetic levitation is essentially a low-efficiency process. Despite the low efficiency of this method, it is worth dealing with it because is one of the few methods that allow melting and obtaining alloys of refractory reactive metals. The research was limited to the analysis of the electromagnetic field modeled three-dimensionally. From among of 245 variants considered in the article, the most promising one was selected characterized by the highest efficiency. This variant will be a starting point for further work with the use of optimization methods.

Sign in / Sign up

Export Citation Format

Share Document