bluuebunny/crossref_metadata_2025_split huggingface dataset:
Thief Zone Characterization and its Impact on Well Performance Based on Surveillance Data, Experimental Data and Theoretical Analysis for a Carbonate Reservoir
Authors: Wei Chenji , Zheng Jie , Ouyang Xiaohu , Ding Yutao , Ding Mingming , Lin Shiyao , Song Hongqing 
Published: September 2019

Abstract
Understanding the heterogeneity is critical for a successful water injection in a carbonate reservoir. Thief zone is one of the most obvious forms of heterogeneity, which indicates the thin layer with higher permeability compared to the average reservoir permeability. The existence of thief zone results in earlier water breakthrough and faster water cut increase, which then lead to lower sweep efficiency and smaller recovery factor. Therefore, determining the distribution of thief zone and its impact towards production, and proposing a corresponding development plan are very important.
In this paper, a novel method is established to determine the thief zone distribution based on dynamic surveillance data. A new index is proposed as the relative contribution index to characterize the relative contribution of a certain layer, which is fundamental for thief zone determination. In addition, effect on water flooding development of thief zone's location is studied by experimental and theoretical analysis. The changes of water cut and production rate are analyzed under different conditions such as location of the thief zone, injection rate, and variogram. Finally, optimized development strategy is proposed to deal with the existence of thief zone.
Distribution of thief zone is characterized based on the proposed method, which indicates that thief zone development has intimate relationship with depositional facies and diagenesis. Experimental and theoretical analysis results show that the present model considering stratified water-flood is consistent with the experimental results. The water displacement effect is the best when the thief zone is located in the upper reservoir. This paper also points out the optimal adjustment period for water shutoff and profile control of the reservoir with thief zones. In addition, the greater the injection rate, the faster the water cut increase. Furthermore, the smaller the variogram, the slower the water cut increase, and the later the water breakthrough time.
This study provides a method to characterize thief zone, which can be used as a reference for similar oilfield development. In addition, it provides a quick and reasonable guide in the later adjustment of water flooding development of carbonate reservoirs with thief zones.
EOS Workflows Uncertainties and Implications in Reservoir Modeling
Authors: Angulo Yznaga Reinaldo Jose 
Published: September 2019

Abstract
Data from seismic to production is integrated to build models to provide estimations of parameters such as petroleum volumetrics, pressure behavior, and production performance (Fig. 1). The level of confidence of these models depends on the representativeness of the data. The quality of the generated models is based on the data interpreted and integrated aimed to build computational realizations of petroleum reservoirs.
Reservoir dynamic simulation is the most applied process that integrates all reservoir data, where an Equation of State (EOS) is coupled with the objective to estimate the fluid thermodynamic state at each computational step. The simulation consists of iterative mathematical computations in which the reservoir-defined conditions at the previous time step is an input to determine the properties at the next and subsequent time steps. The calculated pressure is a fundamental variable in each time step, which means that a representative and high level of confidence Pressure Volume Temperature (PVT) model is required to avoid scale-up of errors resulting from fluid pressure estimation.
A PVT modeling includes three main stages: Fluid sample and data acquisitionLaboratory analysis and fluid characterizationThe EOS model.
The emphasis in this work is on the EOS model, which is the fluid model used for the simulation process. The objective of this work is to analyze the main uncertainties associated with typical EOS modeling and defining the level of confidence of these EOS approaches. In this work, some of the most-used approaches for EOS modeling are reviewed. An assessment of these methods is also provided based on their application to actual petroleum fluids with the objective of defining their statistical level of confidence.
First, the study analyzes the sources of critical uncertainties in a PVT EOS model. Second, a statistical number of PVT laboratory studies of petroleum fluids is used to determine the level of confidence of four approaches that are based on the two well-known Peng-Robinson and Soave-Redlich-Kwong EOS. Third, statistical analysis is performed to determine the level of confidence of the different methods. Fourth, a correlation to determine the optimal number of pseudo-components is defined. These steps include: Characterization of fluid and heavy componentsTuningLumping.
As a result of this study, one can conclude: The level of confidence of the four analyzed approachesThe significance of the difference between the analyzed methodsA correlation to determine the optimal number of pseudo-components.
In this work, a statistical analysis over some of the most-used EOS modeling approaches and on a set of petroleum fluid PVTs was performed to determine the level of confidence of four EOS modeling methods. In addition, a correlation was introduced for a priori determination of the optimal number of pseudo-components in a PVT fluid.
Pitfalls of 3D Saturation Modelling in the Middle East
Authors: O'Meara Daniel J. 
Published: September 2019

Abstract
This paper shows how greater scientific rigor in discussions of modelling 3D saturations in the Middle East can lead to better understanding of the reservoirs. It demonstrates with examples how vocabulary limits ability to solve problems related to saturations, compartmentalization, and permeability. It raises the bar on technical discussions of saturation.
"Saturation-height modelling", "transition zones", and "Thomeer hyperbolas" are examples of terms that repeatedly confuse discussions of modelling 3D saturations in the Middle East. Vocabulary exposes a lack of scientific rigor, impedes progress, and leads to notable failures. Saturation is not merely a function of height. At the very least, it also depends on porosity, permeability, fluid densities, interfacial tension, and contact angle. Limiting it to height requires adding in all of these other functionalities as afterthoughts rather than incorporating them naturally through dimensional analysis. Most glaringly, it obscures the very useful role that saturations have in constraining permeability modelling and identifying reservoir compartments.
"Transition zones" focus on saturation and take emphasis away from relative permeability and fractional flow. Bimodal pore systems (abundant in the Middle East) can have such low relative permeability to water at high saturations that even 70% water saturation can produce dry oil. In such cases, talk of a transition zone is counterproductive as it implies high water production.
"Thomeer hyperbolas" reveal biases in how to fit capillary pressure curves. Force-fitting all data with a single model is inadequate. It takes emphasis away from understanding pore systems of rocks in favor of promoting a single-minded view. These examples and their implications are discussed in detail.
The existing literature is replete with incomplete explanations and misunderstandings that lead to notable failures in modelling Middle Eastern fields. Understandings predicated on simplified descriptions of homogeneous reservoirs are no longer sustainable. A more scientifically rigorous methodology is presented.
Rigorous Multi-Scenario Uncertainty Analysis: An Easy Way to Create an Ensemble of Many Concepts, with Hundreds of Uncertainties, and the Power of the Cloud to Evaluate Thousands of Realizations in Hours
Authors: Ashoori Elham , Van der Steen Erik H. 
Published: September 2019

Abstract
When key geological scenario uncertainties, captured in multiple conceptual models, are combined with continuous parameters, the evaluation of a representative sample set quickly becomes unmanageable, laborious and too time consuming to execute. A workflow is presented that enables users to easily model conceptual as well as parametric uncertainties of the reservoir without the necessity of any complex scripting. The chain of models for all concepts is presented in one view, to provide overview of the key differences between concepts used. An ensemble of geologically sound samples can be created taking into account parameter dependencies and probabilities of concepts. The chain of models per concept can easily be (re)executed.
A case study is presented that consists of multiple concepts based on different hierarchical stratigraphic models in combination with different fault models, each of which with its own fluid- (defined contacts per compartment), grid- (sub-layering and areal resolution) and rock property models. Volumetric calculations are run on an ensemble to get static model observables like GRV, Pore Volume, Oil-In-Place, etc., reported by multiple sub-regions of the model in combination with a lease boundary. (When coupled with dynamic simulation, observables like ultimate recovery, break-through timing, etc. could also be obtained). As thousands of realizations were run concurrently, run time was reduced from weeks to hours. Results reveal the distribution and dependency of observables like GRV on top-structure-depth uncertainty and contact-level uncertainty. For in-place volumes the full suite of concepts and other parametric uncertainties including the stochastic uncertainties (i.e. seed) is analyzed. This also enables the identification of the key uncertainties that impact equity the most, which can be of great commercial value during equity negotiations. This workflow demonstrates how, with the power of Cloud computing, rigorous evaluation of multiple concepts combined with many parametric uncertainties has been achieved within practical turn-around times. As such it overcomes the prohibitive hurdles of the past that often have led to simplifications necessary to save time and effort. The result is better decision quality in resource development decisions.
Production Optimisation Under Uncertainty with Automated Scenario Reduction: A Real-Field Case Application
Authors: Barros E. , Fonseca R. M. , de Moraes R. J. 
Published: September 2019

Abstract
In this work we discuss the successful application of our previously developed automated scenario reduction approach applied to life-cycle optimization of a real field case. The inherent uncertainty present in the description of reservoir properties motivates the use of an ensemble of model scenarios to achieve an optimized robust reservoir development strategy. In order to accurately span the range of uncertainties it is imperative to build a relatively large ensemble of model scenarios. The size of the ensemble is directly proportional to the computational effort required in robust optimization. For high-dimensional, complex field case models this implies that a large ensemble of model scenarios which albeit accurately captures the inherent uncertainties would be computationally infeasible to be utilized for robust optimization. One of the ways to circumvent this problem is to work with a reduced subset of model scenarios. Methods based on heuristics and ad-hoc rules exist to select this reduced subset. However, in most of the cases, the optimal number of model realizations must be known upfront. Excessively small number of realizations may result in a subset that does not always capture the span of uncertainties present, leading to sub-optimal optimization results. This raises the question on how to effectively select a subset that contains an optimal number of realizations which both is able to capture the uncertainties present and allow for a computationally efficient robust optimization. To answer this question we have developed an automated framework to select the reduced ensemble which has been applied to an original ensemble of 300 equiprobable model scenarios of a real field case. The methodology relies on the fact that, ideally, the distance between the cumulative distribution functions (CDF) of the objective function (OF) of the full and reduced ensembles should be minimal. This allows the method to determine the smallest subset of realizations that both spans the range of uncertainties and provides an OF CDF that is representative of the full ensemble based on a statistical metric. In this real field case application we optimize the injection rates throughout the assets life-cycle with expected cumulative oil production as the OF. The newly developed framework selected a small subset of 17 model scenarios out of the original ensemble which was used for robust optimization. The optimal injection strategy achieved an average increase of 6% in cumulative oil production with a significant reduction, approximately 90%, in the computational effort. Validation of this optimal strategy over the original ensemble lead to very similar improvements in cumulative oil production, highlighting the reliability and accuracy of our framework.
Optimization of Oil Field Development using a Surrogate Model: Case of Miscible Gas Injection
Authors: Simonov Maksim , Shubin Andrei , Penigin Artem , Perets Dmitrii , Belonogov Evgenii , Margarit Andrei 
Published: September 2019

Abstract
The topic of the paper is an approach to find optimal regimes of miscible gas injection into the reservoir to maximize cumulative oil production using a surrogate model. The sector simulation model of the real reservoir with a gas cap, which is in the first stage of development, was used as a basic model for surrogate model training. As the variable (control) parameters of the surrogate model parameters of gas injection into injection wells and the limitation of the gas factor of production wells were chosen. The target variable is the dynamics of oil production from the reservoir. A set of data has been created to train the surrogate model with various input parameters generated by the Latin hypercube.
Several machine learning models were tested on the data set: ARMA, SARIMAX and Random Forest. The Random Forest model showed the best match with simulation results. Based on this model, the task of gas injection optimization was solved in order to achieve maximum oil production for a given period. The optimization issue was solved by Monte Carlo method. The time to find the optimum based on the Random Forest model was 100 times shorter than it took to solve this problem using a simulator. The optimal solution was tested on a commercial simulator and it was found that the results between the surrogate model and the simulator differed by less than 9%.
Multivariate 3D Static and Dynamic Modeling Approach for an Oil Field at Early Stages of Exploration for a Fast-Track Project
Authors: Litvin Vitaly , Potapov Alexander , Snachev Mikhail 
Published: September 2019

Abstract
In this paper, the approach to multivariate static and dynamic modeling is considered on the example of an offshore field discovered in 2017. Based on the limited volume of information, the quantitative and qualitative description of uncertainties included further in the 3D modeling is made. This model is proposed to be used as a tool for prompt decision making when implementing a fast-track project with limited time between exploration and pre-FEED stages.
Optimization of Waterflooding Strategy Using Artificial Neural Networks
Authors: Bruyelle Jérémie , Guérillot Dominique 
Published: September 2019

Abstract
Waterflooding is the main technic to recover hydrocarbons in reservoirs. For a given set of wells (injectors and producers), the choice of injection/production parameters such as pressures, flow rates, and locations of these boundary conditions have a significant impact on the operating life of the wells. As a large number of combinations of these parameters are possible, one of the critical decision to make is to identify an optimal set of these parameters. Using the reservoir simulator directly to evaluate the impact of these sets being unrealistic considering the required number of simulations, a common approach consists of using response surfaces to approximate the reservoir simulator outputs. Several techniques involving proxies model (e.g., kriging, polynomial, and artificial neural network) have been suggested to replace the reservoir simulations. This paper focalizes on the application of artificial neural networks (ANN) as it is commonly admitted that the ANNs are the most efficient one due to their universal approximation capacity, i.e., capacity to reproduce any continuous function. This paper presents a complete workflow to optimize well parameters under waterflooding using an artificial neural network as a proxy model. The proposed methodology allows evaluating different production configurations that maximize the NPV according to a given risk. The optimized solutions can be analyzed with the efficient frontier plot and the Sharpe ratios. An application of the workflow to the Brugge field is presented in order to optimize the waterflooding strategy.
Assessment of H2S Production Risks in Heterogeneous Reservoirs Using Laboratory-Calibrated Compositional Thermal Reactive Simulations
Authors: Gasser-Dorado Julien , Ayache Simon Victor , Lamoureux-Var Violaine , Preux Christophe , Michel Pauline 
Published: September 2019

Abstract
SAGD is commonly used as a thermal EOR method to produce heavy oil. However it suffers from the production of acid gases formed by aquathermolysis chemical reactions that occur between the steam, the sulfur-rich oil and the mineral matrix. The objectives of this paper are to take advantage of a comprehensive chemical model coupled to compositional thermal reservoir simulations to predict and understand the H2S production variation at surface according to the type of reservoir.
Thermal reservoir simulations coupled to both a SARA based 10-component / 5-reaction chemical model fully calibrated against laboratory data and a compositional PVT are used to simulate SAGD processes on heavy oil fields in Athabasca, Canada. Numerical results are then analyzed to provide a comprehensive analysis of the mechanisms leading to in-situ H2S generation and its production at wellheads based on compositional thermal simulations coupled to a fully laboratory calibrated SARA-based chemical model. Composition of the pre-steam, post-steam and produced oil are compared to understand the effect of the aquathermolysis reactions. The impact of heterogeneities on H2S production both in-situ and at surface can also be observed and explained, especially the variations in vertical permeability. Then simple reservoir models with two facies are used to further understand the impact of heterogeneities on H2S production at surface. Overall heterogeneous cases show important changes in the temperature distribution, fluid flows, reactions kinetics and steam chamber shape that lead to H2S production variations at surface. This detailed description of the involved mechanisms in acid gases production will allow operators to better forecast their H2S risks according to their reservoir properties.
3D Model Validation in Reservoir Management Optimization Workflow
Authors: Luciani Italo , Renna Simona , Ortega Angelo , Catalani Carlo , Mastrogeorgiou Konstantinos , Cortese Andrea 
Published: September 2019

Abstract
3D model is a valuable tool in reservoir management, provided its representativeness of reservoir dynamics.Traditional History Match mainly focuses on reproducing reservoir behavior at well scale. A good match is not always representative of fluid movements in the reservoir. The proposed approach for 3D model validation combines and compares the results of integrated production analysis, in particular flow paths identification, with history matching by using streamlines technology. Streamlines speed up the comparison process especially in complex 3D models.
The workflow is based on a massive Production Data Analysis (PDA) where geological and dynamic data are integrated to identify preferential paths followed by the different fluid phases during the producing life of the field. The main result is the Fluid Path Conceptual Model (FPCM) where aquifer and injected water movements are clearly identified. Once the flooded areas are detected, streamlines are traced on the history matched model in order to easily compare the simulated connections with hard information from PDA. Actions to improve the model representativeness are suggested and integrated in an iterative tuning process.
This paper presents the results of the methodology applied on two complex fields with different injection strategies. FPCMs resulting from PDA provided a powerful boost to drive the history match and speed up the whole process. Priority was given in reproducing the identified preferential paths rather than to perfectly match well production data (which can be also affected by allocation uncertainties) by means of local unrealistic adjustments.
Streamlines were run on Intersect simulation, proving to be a fast and powerful tool for the visualization and understanding of fluid movements in the 3D Model. Since streamlines are used as visualization tool and are traced on a corner point geometry grid using fluxes provided by reservoir simulation, the reliability of the simulation output is preserved.
Once the model is representative of the real field behavior, it can be used as predictive tool in Reservoir Management to optimize the current injection strategy, promoting most efficient injectors.