bluuebunny/crossref_metadata_2025_split huggingface dataset:
Aquifer Influx and Reservoir Connectivity Evaluation through Surveillance Data Analysis for a Large Sandstone Multi-Layered Reservoir
Authors: Yong Li , Baozhu Li , Jiasheng Zhou 
Published: May 2017

Abstract
M1 reservoir is a large multi-layered sandstone reservoir in Middle East, which is under primary depletion and edge aquifer drive. There are lots of sources of water production data in M1, and water production data are one of the most important and invaluable surveillance data to understand reservoir connectivity. This paper proposes a method to show how to integrate all sources of aquifer influx surveillance data to evaluate reservoir connectivity of M1.
There are four types of aquifer influx identification data in M1 reservoir, and different type of surveillance data are analyzed in detail. Through aquifer influx analysis, it can be confirmed if wells are aquifer flooded in some zones. Then, combined geological understanding with well aquifer breakthrough time and well water cut change characteristic analysis, the possible aquifer influx zone is determined. Finally, aquifer support and sand body connectivity around water flooded wells are better understood, which is helpful and useful for next waterflooding development.
M1 reservoir is a large multi-layered sandstone reservoir of deltaic environment with oil bearing area around 500Km2 in Middle East. And M1 is influenced by fluvial, tide and wave, which results in great variations of sand bodies' distribution, reservoir quality and connectivity. Furthermore, M1 reservoir is still under primary depletion with reservoir pressure close to saturation pressure, so waterflooding should be applied urgently. Four types of data were analyzed to study the aquifer influx, which including measured water cut data, flowtest data, saturation logging data and SGS data. Through data analysis, the confirmed aquifer-influx wells and possible aquifer -influx wells are determined, and water breakthrough time and water cut change characteristic are also determined. And based on the characteristic, four areas with different characteristic are classified. Combined with the geological understanding, it is found that the connectivity within each area are similar, but there are barriers among different areas which results in poor communication among different areas. Also the water breakthrough zone of each area are different, and it is useful to understanding aquifer support and reservoir lateral heterogeneity of different zones. Furthermore, aquifer influx has preferred direction, which mainly moves along with the channels axis. This phenomenon should be considered in well pattern decision making during the following waterflooding study.
This paper offers a case study of reservoir connectivity analysis based on different types of aquifer influx surveillance data analysis. And the understanding is also much valuable and useful for depositional facies mapping and the next waterflooding well pattern selection and decision. It also provide a reference for the related study on other similar field.
Multiscale Adapted Time-Splitting Technique for Nonisothermal Two-Phase Flow and Nanoparticles Transport in Heterogenous Porous Media
Authors: El-Amin Mohamed F. , Kou Jisheng , Sun Shuyu 
Published: May 2017

Abstract
This paper is devoted to study the problem of nonisothermal two-phase flow with nanoparticles transport in heterogenous porous media, numerically. For this purpose, we introduce a multiscale adapted time-splitting technique to simulate the problem under consideration. The mathematical model consists of equations of pressure, saturation, heat, nanoparticles concentration in the water–phase, deposited nanoparticles concentration on the pore–walls, and entrapped nanoparticles concentration in the pore–throats. We propose a multiscale time splitting IMplicit Pressure Explicit Saturation–IMplicit Temperature Concentration (IMPES-IMTC) scheme to solve the system of governing equations. The time step-size adaptation is achieved by satisfying the stability Courant–Friedrichs–Lewy (CFL&lt;1) condition. Moreover, numerical test of a highly heterogeneous porous medium is provided and the water saturation, the temperature, the nanoparticles concentration, the deposited nanoparticles concentration, and the permeability are presented in graphs.
History to Forecast Transition Challenges in Reservoir Simulation of Giant Oilfields
Authors: Mohmed Farzeen , Patacchini Leonardo 
Published: May 2017

Abstract
While history matching of a reservoir simulation model principally aims at reproducing past shut-in pressure and fractional flow observations, transition from history to forecast further requires honoring the productivities (or injectivities) of wells, and implementing the current field operational strategy to avoid unwanted reallocation of rates.
Well-level matching is particularly lengthy for giant oilfields with several hundreds to thousands of completions, hence sometimes not refined enough to warrant using observed flowing pressures as forecast constraints. Furthermore, breakthrough times are not necessarily replicated in all wells; this strongly increases discrepancies between measured and simulated flowing pressures, and can cause spurious shutting of wells infringing on reservoir management guidelines.
Short-term forecast confidence can be improved by compensating well-level mismatches observed at the end of history for production and injection control purposes only, without solving the inherent model problems. The purpose of this paper is to describe a workflow followable with most commercial reservoir simulators to achieve such objective, forcing production and injection continuity at the onset of forecast while keeping the choked margin of wells producing below capacity.
A successful application of the workflow is illustrated using the model of a giant Middle Eastern carbonate oilfield with more than 150 active production strings, using an eight-year (2015–2023) drilling schedule followed by no further activity. In this example, confidence in long-term field-level forecasts does not deteriorate after applying the proposed well-level compensations.
Prediction of Polymer Injectivity in Damaged Wellbore by Using Rheological Dependent Skin
Authors: Behr Aron , Rafiee Mohsen 
Published: May 2017

Abstract
In polymer flooding, the polymer solution injectivity is a key element to estimate the success of EOR measure, which is highly related to the polymer viscosity. Because of the high shear rate near the injector wells, the polymer viscosity changes considerably due to the non-Newtonian rheological behaviour. The existing well inflow/outflow models implemented in numerical reservoir simulators are not flexible enough to consider strong shear thinning effect without building extremely fine grids. Therefore, the predicted pressure difference in the vicinity of the wells is usually overestimated.
In this paper, the way to improve the quality of the polymer injectivity calculation within the reservoir model with a coarse Cartesian grid is suggested. The idea of introducing a negative skin factor into the inflow model is extended and applied for damaged wells (in which positive skin is available in the existing model) or wells with a reduced contact surface with the formation layers due to perforation. To cover a non-uniform and high speed flow in the vicinity of discrete distributed perforation openings, special approximations for a predefined transition zone are considered. In all derivations of the analytical solutions, the power law rheology of the polymer is assumed.
The method was adapted to the commercial reservoir simulators Eclipse 100 and CMG STARS with different viscosity representative radii in the polymer inflow model. The impact of the improvements on the numerically reproduced injection of non-Newtonian fluid is tested on a synthetic box model as well as on a real field model by injection of the biopolymer Schizophyllan which exhibits a remarkable shear thinning behaviour.
Depending on the grid size resolution, the results show significant improvement in polymer injectivity prediction. Advantage of the approach becomes more distinct, as the size of the well grid block increases, which allows to significantly reduce the CPU run time.
A New Technique to Develop Rock Strength Correlation Using Artificial Intelligence Tools
Authors: Tariq Zeeshan , Elkatatny Salaheldin , Mahmoud Mohammed , Ali Abdelwahab Z. , Abdulraheem Abdulazeez 
Published: May 2017

Abstract
Unconfined compressive strength (UCS) is the key parameter to; estimate the insitu stresses of the rock, design optimal hydraulic fracture geometry and avoid drilling problems like wellbore instability. UCS can be estimated by rock mechanical tests on core plugs retrieved from the depth of interest but retrieving reservoir rock samples throughout the depth of the reservoir section and performing laboratory tests on them are extremely expensive as well as time consuming. In absence of core plugs, UCS can be estimated from empirical correlations. Most of the empirical correlations for UCS prediction reported in the literature are based on elastic parameters or on compressional wave velocity. These correlations were developed using linear or non-linear regression techniques. Artificial intelligence tools once optimized for training can successfully model UCS since these tools can capture highly complex and non-linear relationship between input parameters and the output parameter.
The objective of this research study is to accurately predict UCS of rock using basic geophysical well logs namely; bulk density, compressional, and shear wave velocities, by applying different artificial intelligence techniques namely; Support Vector Machine (SVM), Adaptive neuro fuzzy inference system (ANFIS) and Artificial neural network (ANN).
The data set used in this study, comprised of 200 laboratory measured UCS values on core plugs and their corresponding well logs. The data were collected from 10 wells which were located in a giant carbonate reservoir. Based on minimum average absolute percentage error (AAPE) and highest coefficient of determination (R2) between actual and predicted data, ANN model proposed as the best model to predict UCS. A rigorous empirical correlation was developed using the weights and biases of ANN model to predict without the need of any software incorporating AI. A comparison of proposed model with other correlations to predict UCS on new data set also suggested that the proposed model gives less AAPE. Therefore, the proposed model seems very promising and can serve as a handy tool to help geo-mechanical engineers to determine the UCS of the carbonate rock.
Uncertainty Assessment in Production Forecasting and Optimization for a Giant Multi-Layered Sandstone Reservoir Using Optimized Artificial Neural Network Technology
Authors: Wei Chenji , Wang Yuhe , Ding Yutao , Li Yong , Shi Jing , Liu Shuangshuang , Tian Changbing , Li Baozhu , Xiong Lihui , Zhang Qi 
Published: May 2017

Abstract
This paper presents an uncertainty assessment project using Artificial Neural Network (ANN) for a giant multi-layered sandstone reservoir in Middle East, which contains several uncertainties and associated risks. Uncertainty quantification in history matching, production forecasting and optimization approaches often requires hundreds of thousands of forward flow simulations to explore the uncertain parameter space, causing forbidden computational time requirement, especially for large-scale reservoir models. In order to bypass this limitation, one can use a proxy to replace the time-consuming flow simulator. In this work, an optimized ANN is used as the proxy and an uncertainty assessment workflow is implemented for the giant Cretaceous multi-layered sandstone reservoir using a global optimizer. Using the ANN based uncertainty assessment framework, the impacts of the main uncertain parameters on production forecasting are assessed for this multi-layered sandstone reservoir. Then, field development optimization is also performed to optimize wells injection and production rates to maximize the economic measures considering uncertainties. Using a real field as a case study, the capability of our uncertainty assessment workflow is demonstrated. The optimized ANN successfully captures the complex nonlinear dynamics as a proxy and significantly reduced the computation time.
Dynamic Permeability Models in Dual-Porosity System for Unconventional Reservoirs: Case Studies and Sensitivity Analysis
Authors: Liang Yu , Sheng Jie , Hildebrand Jeffery 
Published: May 2017

Abstract
Dynamic Permeability is one of the most important key features for unconventional reservoir. The production of unconventional reservoirs, such as shale oil and gas, tight oil and gas, coal bed methane etc., heavily depends on the effective permeability of fracture. However, its permeability changes nonlinearly due to dual-porosity system, which affects the accuracy of unconventional reservoir simulation. This paper investigates two commonly accepted dynamic permeability models in details, S&D model and G&W model. Furthermore, these models are compared and applied in field cases. This study can help us understand the physics behind dynamic permeability in dual porosity system, and facilitate applying dynamic permeability model from core scale to field scale.
Firstly, unconventional oil and gas recovery process in dual porosity system is analyzed to determine the key petrophysical controlling factors. In particular, the relation between permeability and fracture porosity is analyzed and discussed in details. Then the most popular two models, S&D model and G&W model, are compared and analyzed. The advantages and disadvantages of those models are discussed, including gas desorption effect, matrix shrinkage effect, micropososity change effect. In addition, the models are applied to several field cases, and the results of these two models are discussed and compared with each other. Finally, the models are applied to pressure transient analysis, to test their feasibility in unconventional reservoir simulation.
By analyzing the model, the important role of fracture porosity in permeability prediction is addressed. The results of cases study validate the accuracy of G&W model, which can give a better prediction of dynamic permeability, and perfectly describe rebounding pressure phenomenon as pore pressure decreases. For different models, the sensitivities of several parameters in geomechanical equations and Langmuir isothermal equation are analyzed by Monte Carlo method, and these key sensitive parameters have impact on the simulation results for unconventional reservoirs.
From geomechanical perspective, the effects of fracture porosity on permeability change are investigated and discussed in details, which is critical in dual porosity system. The field cases and relevant sensitivity analyses are essential in understanding the physics behind the problem. By comparison and applications of the models, it is concluded that dynamic permeability models can be applied to unconventional reservoir simulation.
Characterization and Uncertainty Reduction on Facies Distribution and Probability Cubes with Ensemble Kalman Filter History Matching
Authors: Abadpour Anahita , Adejare Moyosore , Chugunova Tatiana , Deboaisne Renaud 
Published: May 2017

Abstract
Power of ensemble based data assimilation methods to history match oil and gas reservoirs has been revealed during the past couple of years by different field application works (Oliver and Chen, 2011). Moreover the achievements in advanced parameterization (Lorentzen et al. 2011) and (Hanea et. al. 2014) to deliver posterior models which respect prior geology drastically enhanced the robustness of the procedures. In this study a huge filed case represented by a mega geological model has been the candidate for assisted history matching with ensemble based techniques, ES-MDA (Emrick, Raynold 2013) in particular.
To insure the geological realism of history matched models, a parameterization approach similar to (Hanea et. al. 2014) has been adapted and improved, as the initial workflow for lithological modeling has been based on truncated Gaussian simulations (TGS). Adding 3D uncertain facies proportion distributions which has been produced by stochastic inversion of seismic information was the way to enrich and ameliorate the method. These proportions have been used as the base of truncation procedure in TGS.
In this work we showed how to use geological and geophysical based uncertainties in reservoir modeling, and with an appropriate parameterization, one could obtain fully realistic models which honors all a priori information and also matches the field measurement data.
The main challenge with proposed approach was to insure honoring the well data which is normally constraining data in facies modeling procedure, taking into account that both underlying Gaussian variable and proportion values are modified by data assimilation process. To insure such a conditioning certitude, all well data has been reflected in probability maps by an a priori kriging on well location and closed by areas. This strategy guaranteed unchanged well data conditioning on all final realizations.
Determination of Three-Phase Relative Permeability in CHOPS Processes by Use of an Improved Iterative Ensemble Smoother
Authors: Fan Zhaoqi , Yang Daoyong , Li Xiaoli 
Published: May 2017

Abstract
In this paper, a robust and pragmatic technique has been proposed to determine the three-phase relative permeability of cold heavy oil production with sand (CHOPS) processes by comprehensively incorporating dynamic wormhole growth and foamy oil flow with an improved iterative ensemble smoother (IES), which is achieved by normalizing both model parameters and measured production profiles. More specifically, a recently proposed pressure-gradient-based sand failure criterion has been applied to characterize the dynamic wormhole propagations, while the evolution of foamy oil has been achieved through kinetic reactions embedded in a reservoir simulator. Only the three-phase relative permeability of the wormhole network has been parameterized using a power-law model since the multi-phase flow within the wormholes dominates, in particular, production performance of the CHOPS processes. Then, the model parameter matrix consisting of coefficients of the power-law model, the pressure-gradient-based sand failure criterion, and the foamy oil reactions can be inversely and implicitly estimated through the improved IES algorithm, provided that the production profiles of the CHOPS processes are made available. Effects of the wormhole growth and the foamy oil flow on the three-phase relative permeability are implicitly examined through the integrated and improved model parameter matrix. Subsequently, the newly proposed technique was validated by use of a CHOPS experiment in the literature. In addition to the converged three-phase relative permeability, the well matched experimental measurements (i.e., cumulative oil, gas, and sand production) have synergistically proven that the newly proposed method is qualified for accurately determining the three-phase relative permeability in the CHOPS processes. In order to reproduce the transition stage on production profiles, both one set and two sets of three-phase relative permeability were used during the entire production period, though the former fails to do so. It has been substantiated that two sets of the three-phase relative permeability are able to reproduce the transition stage through considering the effects of sand failure and foamy oil of the CHOPS processes. In addition, residual oil saturation of the wormhole network is found to dramatically reduce once the sand production is initialized. Also, it is found that gas relative permeability is more sensitive to the gas saturation for the second set, though it is relatively smaller than that of the first set.
Highly Accurate Simulation of Frontal Instabilities at Extremely Large Mobility Contrasts in Solvent-Based Oil Recovery
Authors: Yuan Qingwang , Wang Jinjie , Yao Shanshan , Zeng Fanhua , Knorr Kelvin D. , Imran Muhammad 
Published: May 2017

Abstract
Solvent-based processes have great potential to enhance oil recovery. The injected solvent is able to significantly reduce the viscosity of the oil and improve its mobility. In displacement processes, the frontal instability happening at the interface between solvent and oil can increase their surface contact area, while it also leads to earlier breakthrough time and less sweep efficiency if such instabilities develop too severely. Especially at very large mobility contrasts, the less viscous injected solvent can easily bypass the highly viscous heavy oil. Most of the previous studies on simulations of frontal instability are restricted to a small range of mobility ratio. In the present study, we adopted a highly accurate numerical method to simulate the detailed development of solvent fingering at extremely large mobility ratios in fully miscible displacements. At certain conditions, the case with a maximum mobility ratio as large as 22026.5 could be successfully modeled. This allowed analysis of the flow dynamics and mass transfer at such a large mobility contrast in miscible displacement processes. The breakthrough time and sweep efficiency at breakthrough were also investigated at small and extremely large values of mobility ratio through numerical simulations. With an increase in mobility ratio, the breakthrough time decreased, first very rapidly and then slowly. The breakthrough time varied as a power relationship with the mobility ratio for various Peclet numbers. Similar relationships were found for the sweep efficiency versus mobility ratio. This approach would be helpful for the accurate prediction of breakthrough time and sweep efficiency in miscible displacement under extremely large mobility contrasts.
Factors Influencing Corn Canopy Throughfall at the Row Scale in Northeast China
Authors: Sun Zenghui , Li Zizhong , Li Baoguo , Sun Tao , Wang Huanxi 
Published: May 2017

Core Ideas
Throughfall was statistically different among five measurement locations.
Cumulative throughfall of 24 to 36 cm from row exceeded that of 0 to 12 cm and 48 to 60 cm by 46 to 88%.
Coefficient of variation of throughfall decreased with rainfall amount and increased with leaf area index and plant height.
Understanding throughfall (TF) and its influencing factors at crop row scale is a key step to reveal the mechanisms of soil water recharging, soil erosion, and evaporation of intercepted rainfall. The objective of this study was to determine the amount and the variation of TF in relation to plant canopy and rainfall. In this study, TF was measured by a rectangular plexiglass collector at five measurement locations (MLs) with the distances of the (A) 0 to 12 cm, (B) 12 to 24 cm, (C) 24 to 36 cm, (D) 36 to 48 cm, and (E) 48 to 60 cm from corn (Zea mays L.) row during the period of corn plant height growth (HT) approximately from 50 to 250 cm when the leaf area index (LAI) increases from 0.4 to 4.0 in 2013 and 2014. Throughfall increased with total rainfall amount (RA) but decreased with LAI and HT values. The lowest TF values were recorded at the MLs of 0 to 12 cm and 48 to 60 cm near the corn row, and the highest TF values occurred at the ML of 24 to 36 cm at the center of between row areas. The cumulative TF at the ML of 24 to 36 cm exceeded that at the MLs of 0 to 12 cm and 48 to 60 cm by approximately 88 and 46% in 2013 and 2014, respectively. These results have potential values in irrigation scheduling and agrochemical application in the Corn Belt of Northeast China (CBNC).