Streamline Assisted Ensemble Kalman Filter - Formulation and Field Application

The goal of any data assimilation or history matching algorithm is to enable better reservoir management decisions through the construction of reliable reservoir performance models and the assessment of the underlying uncertainties. A considerable body of research work and enhanced computational capabilities have led to an increased application of robust and efficient history matching algorithms to condition reservoir models to dynamic data. Moreover, there has been a shift towards generating multiple plausible reservoir models in recognition of the significance of the associated uncertainties. This provides for uncertainty analysis in reservoir performance forecasts, enabling better management decisions for reservoir development. Additionally, the increased deployment of permanent well sensors and downhole monitors has led to an increasing interest in maintaining 'live' models that are current and consistent with historical observations. One such data assimilation approach that has gained popularity in the recent past is the Ensemble Kalman Filter (EnKF) (Evensen 2003). It is a Monte Carlo approach to generate a suite of plausible subsurface models conditioned to previously obtained measurements. One advantage of the EnKF is its ability to integrate different types of data at different scales thereby allowing for a framework where all available dynamic data is simultaneously or sequentially utilized to improve estimates of the reservoir model parameters. Of particular interest is the use of partitioning tracer data to infer the location and distribution of target un-swept oil. Due to the difficulty in differentiating the relative effects of spatial variations in fractional flow and fluid saturations and partitioning coefficients on the tracer response, interpretation of partitioning tracer responses is particularly challenging in the presence of mobile oil saturations. The purpose of this research is to improve the performance of the EnKF in parameter estimation for reservoir characterization studies without the use of a large ensemble size so as to keep the algorithm efficient and computationally inexpensive for large, field-scale models. To achieve this, we propose the use of streamline-derived information to mitigate problems associated with the use of the EnKF with small sample sizes and non-linear dynamics in non-Gaussian settings. Following this, we present the application of the EnKF for interpretation of partitioning tracer tests specifically to obtain improved estimates of the spatial distribution of target oil

An assessment of subsea production systems

The decreasing gap between technology and the its applicability in the oil industry has led to a rapid development of deepwater resources. Beginning with larger fields where the chances of economic success are high, to marginal fields where project economics becomes a more critical parameter, the petroleum industry has come a long way. However, the ever growing water depths and harsher environments being encountered are presently posing challenges to subsea production. Being able to develop a field and then proceeding to ensure flow for the life of the field comprises many situations where the production equipment can fail and falter or through external factors, be deemed unavailable. Some of the areas where most of the current developments in subsea production are being seen are in subsea processing, flow assurance, long term well monitoring and intervention technologies areas that pose some of the biggest challenges to smooth operation in the deepwater environment. This research highlights the challenges to overcome in subsea production and well systems and details the advances in technology to mitigate those problems. The emphasis for this part of the research is on multiphase pumping, subsea processing, flow assurance, sustained casing pressure problems and well intervention. Furthermore, most operators realize a reduced ultimate recovery from subsea reservoirs owing to the higher backpressure imposed by longer flowlines and taller risers. This study investigates the reasons for this by developing a global energy balance and detailing measures to improve production rates and ultimate recoveries. The conclusions from this energy balance are validated by simulating a deepwater field under various subsea production scenarios

Streamline Assisted Ensemble Kalman Filter - Formulation and Field Application

The goal of any data assimilation or history matching algorithm is to enable better reservoir management decisions through the construction of reliable reservoir performance models and the assessment of the underlying uncertainties. A considerable body of research work and enhanced computational capabilities have led to an increased application of robust and efficient history matching algorithms to condition reservoir models to dynamic data. Moreover, there has been a shift towards generating multiple plausible reservoir models in recognition of the significance of the associated uncertainties. This provides for uncertainty analysis in reservoir performance forecasts, enabling better management decisions for reservoir development. Additionally, the increased deployment of permanent well sensors and downhole monitors has led to an increasing interest in maintaining 'live' models that are current and consistent with historical observations. One such data assimilation approach that has gained popularity in the recent past is the Ensemble Kalman Filter (EnKF) (Evensen 2003). It is a Monte Carlo approach to generate a suite of plausible subsurface models conditioned to previously obtained measurements. One advantage of the EnKF is its ability to integrate different types of data at different scales thereby allowing for a framework where all available dynamic data is simultaneously or sequentially utilized to improve estimates of the reservoir model parameters. Of particular interest is the use of partitioning tracer data to infer the location and distribution of target un-swept oil. Due to the difficulty in differentiating the relative effects of spatial variations in fractional flow and fluid saturations and partitioning coefficients on the tracer response, interpretation of partitioning tracer responses is particularly challenging in the presence of mobile oil saturations. The purpose of this research is to improve the performance of the EnKF in parameter estimation for reservoir characterization studies without the use of a large ensemble size so as to keep the algorithm efficient and computationally inexpensive for large, field-scale models. To achieve this, we propose the use of streamline-derived information to mitigate problems associated with the use of the EnKF with small sample sizes and non-linear dynamics in non-Gaussian settings. Following this, we present the application of the EnKF for interpretation of partitioning tracer tests specifically to obtain improved estimates of the spatial distribution of target oil

Effective permeability upscaling from heterogenous to homogenous porous media

An effective method to upscale permeability is presented to represent a heterogeneous reservoir with homogeneous permeability and porosity values. As a result, there is no need to deal with dual-porosity or dual-permeability models in reservoir simulations. Thus, the required CPU time for reservoir production and flow simulations is reduced significantly

3-Dimensional model making as an innovative tool for enhanced learning through student engagement among early professional medical graduates

Background: Various innovative teaching-learning methods have been introduced in the medical curriculum for a better understanding of the difficult topics. We introduced the 3-dimensional (3D) model-making as an innovative tool for enhanced learning through student engagement among early professional medical graduates. Methods: The study was conducted in the Department of Biochemistry of a Private Medical College. The phase I medical undergraduate students were divided into 20 groups with 10 students in each group. The topics taught by didactic lectures were allotted to each group by lottery method and were informed that the best model will be suitably rewarded after evaluation. Feedback was collected from the students on a five-point Likert scale after the submission and evaluation of the models. Results: About 92% of the students expressed that 3D model-making was an innovative method of learning in the medical profession, and 96.3% agreed that the topics allotted were relevant to the syllabus and helped in better understanding of the subject when compared to didactic lectures. The students also agreed that the 3D model-making activity enhanced their creativity and application of knowledge to learn biochemistry, developed a positive attitude, helped to coordinate with their peers, and improved communication skills. They suggested that this activity should be continued with the inclusion of more topics. Discussion: The 3D model-making activity helped the students to enjoy learning, think differently, understand better, expand their knowledge and recall information more comprehensively

In silico examination of peptides containing selenium and ebselen Backbone To Assess Their Tumoricidal Potential

Introduction: Cancer has been one of the highest causes of morbidity and mortality in the world for decades. Owing to improved therapeutics along with detection, breast cancer mortality has been slowly reducing. The incidence of breast cancer, on the other hand, has increased gradually. More than 100 types of cancer have been identified with a wide range of treatment protocols comprising of chemotherapy, radiation therapy, hormone therapy, etc. In an attempt to curb the serious deleterious effects caused by the chemotherapeutic drugs, numerous peptide molecules are currently popular as alternatives to the standard chemotherapeutic drugs. Methods: In this study, we have carried out in silico investigations to ascertain the anti-proliferative potential of novel peptides based on selenium and ebselen, i.e. Eb-Trp-Asp, 13, Eb-Trp-Glu, 14, and Eb-Trp-Lys, 15. Analysis of protein-ligand interactions, resulting in protein-ligand complex formation, has been carried out using the AutoDockVina in PyRx aided molecular docking technique, which may be an essential indication of druggability of the test peptides. Results: The molecular docking results revealed that the screened ligands had extraordinarily strong binding interactions and affinity for the target. Conclusion: Findings suggested that novel peptide molecule Eb-Trp-Glu, 14 may be a potent anticancer agent

The dynamics of hydraulic fracture water confined in nano-pores in shale reservoirs

Hydraulic fracturing treatments and horizontal well technology are central to the success of unconventional oil and gas development. In spite of this success, replicated over several thousand wells over diverse shale plays, hydraulic fracturing for shale wells remains poorly understood. This includes the poor recovery of hydraulic fracture water, the inability to explain the progressive increases in produced water salinity and an incomplete understanding of the potential trapping mechanisms for hydraulic fracture water. In this work, we focus on describing the distribution of saline water in organic and inorganic pores as a function of pore size and pore morphology with the purpose of providing fundamental insights into above questions. A kerogen model is constructed by mimicking the maturation process in a molecular dynamics simulator and it incorporates structural features observed in SEM images including the surface roughness, tortuous paths, material disorder and imperfect pore openings of kerogen pores. This work also extends this kerogen model through the use of oxygenated functional groups to study fluid behavior in partially mature shales associated with non-zero oxygen to carbon ratios. Our results demonstrate that water entrapment mechanism and the distribution of water and ions in organic and inorganic pores are strongly related to the pore-surface mineralogy and pore width. The work in this paper also underscores the importance of kerogen thermal maturity and pore roughness on the accessibility of the kerogen material to water

Paralellized ensemble Kalman filter for hydraulic conductivity characterization

[EN] The ensemble Kalman filter (EnKF) is nowadays recognized as an excellent inverse method for hydraulic conductivity characterization using transient piezometric head data. Its implementation is well suited for a parallel computing environment. A parallel code has been designed that uses parallelization both in the forecast step and in the analysis step. In the forecast step, each member of the ensemble is sent to a different processor, while in the analysis step, the computations of the covariances are distributed between the different processors. An important aspect of the parallelization is to limit as much as possible the communication between the processors in order to maximize execution time reduction. Four tests are carried out to evaluate the performance of the parallelization with different ensemble and model sizes. The results show the savings provided by the parallel EnKF, especially for a large number of ensemble realizations. (c) 2012 Elsevier Ltd. All rights reserved.The first author acknowledges the financial support from China Scholarship Council (CSC). Financial support to carry out this work was also received from the Spanish Ministry of Science and Innovation through project CGL2011-23295, and from the Universitat Politecnica de Valencia through project PERFORA.Xu, T.; Gómez-Hernández, JJ.; Li ., L.; Zhou ., H. (2013). Paralellized ensemble Kalman filter for hydraulic conductivity characterization. Computers and Geosciences. 52:42-49. https://doi.org/10.1016/j.cageo.2012.10.007S42495

In vitro assessment of adsorbents aiming to prevent deoxynivalenol and zearalenone mycotoxicoses

The high prevalence of the Fusarium mycotoxins, deoxynivalenol (DON) and zearalenone (ZON) in animal feeds in mild climatic zones of Europe and North America results in considerable economic losses, as these toxins affect health and productivity particularly of pigs from all age groups. The use of mycotoxin adsorbents as feed additives is one of the most prominent approaches to reduce the risk for mycotoxicoses in farm animals, and to minimise carry-over of mycotoxins from contaminated feeds into foods of animal origin. Successful aflatoxin adsorption by means of different substances (phyllosilicate minerals, zeolites, activated charcoal, synthetic resins or yeast cell-wall-derived products) has been demonstrated in vivo and in vitro. However, attempts to adsorb DON and ZON have been less encouraging. Here we describe the adsorption capacity of a variety of potential binders, including compounds that have not been evaluated before, such as humic acids. All compounds were tested at realistic inclusion levels for their capacity to bind ZON and DON, using an in vitro method that resembles the different pH conditions in the gastro-intestinal tract of pigs. Mycotoxin adsorption was assessed by chemical methods and distinct bioassays, using specific markers of toxicity as endpoints of toxicity in cytological assays. Whereas none of the tested substances was able to bind DON in an appreciable percentage, some of the selected smectite clays, humic substances and yeast-wall derived products efficiently adsorbed ZON (>70%). Binding efficiency was indirectly confirmed by the reduction of toxicity in the in vitro bioassays. In conclusion, the presented test protocol allows the rapid screening of potential mycotoxin binders. Like other in vitro assays, the presented protocol combining chemical and biological assays cannot completely simulate the conditions of the gastro-intestinal tract, and hence in vivo experiments remain mandatory to assess the efficacy of mycotoxin binders under practical conditions