Integrated Production Data Analysis and Water Injection Optimization in A Giant Carbonate Field

2021 ◽  
Author(s):  
Adel Mehrabadi ◽  
Gabriele Urbani ◽  
Simona Renna ◽  
Lucia Rossi ◽  
Italo Luciani ◽  
...  
Keyword(s):  
Data Analysis ◽  
Water Injection ◽  
Water Flooding ◽  
Integrated Analysis ◽  
Production Data ◽  
Data Set ◽  
Proper Actions ◽  
Injection Scheme ◽  
Vertical Heterogeneity ◽  
Current Water

Abstract In case of giant brown fields, a proper water injection management can result in a very complex process, due to the quality and quantity of data to be analysed. Main issue is the understanding of the injected water preferential paths, especially in carbonate environment characterized by strong vertical and areal heterogeneities (karst). A structured workflow is presented to analyze and integrate a massive data set, in order to understand and optimize the water injection scheme. An extensive Production Data Analysis (PDA) has been performed, based on the integration of available geological data (including NMR and Cased Hole Logs), production (allocated rates, Well Tests, PLT), pressure (SBHP, RFT, MDT, ESP) and salinity data. The applied workflow led to build a Fluid Path Conceptual Model (FPCM), an easy but powerful tool to visualize the complex dynamic connections between injectors-producers and aquifer influence areas. Several diagnostic plots were performed to support and validate the main outcomes. On this basis, proper actions were implemented to optimize the current water injection scheme. The workflow was applied on a carbonate giant brown field characterized by three different reservoir members, hydraulically communicating at original conditions, characterized by high vertical heterogeneity and permeability contrast. Moreover, dissolution phenomena, localized in the uppermost reservoir section, led to important permeability enhancement through a wide network of connected vugs, acting as water preferential communication pathways. The geological analysis played a key role to investigate the reservoir water flooding mechanism in dynamic conditions. The water rising mechanism was identified to be driven by the high permeability contrast, hence characterized by lateral independent movements in the different reservoir members. The integrated analysis identified room for optimization of the current water injection strategy. In particular, key factor was the analysis and optimization at block scale, intended as areal and vertical sub-units, as identified by the PDA and visualized through the FPCM. Actions were suggested, including injection rates optimization and the definition of new injections points. A detailed surveillance plan was finally implemented to monitor the effects of the proposed actions on the field performances, proving the robustness of the methodology. Eni workflow for water injection analysis and optimization was previously successfully tested only in sandstone reservoirs. This paper shows the robustness of the methodology also in carbonate environment, where water encroachment is strongly driven by karst network. The result is a clear understanding of the main dynamics in the reservoir, which allows to better tune any action aimed to optimize water injection and increase the value of mature assets.

Doubling its Production in One Year: Reviving a Brown Field in South of Oman

2021 ◽  
Author(s):  
Suria Amalia Suut ◽  
Mahmood Khamis Al Kalbani ◽  
Issa Quseimi ◽  
Abdullah Gahaffi ◽  
Arjen Wielaard ◽  
...  
Keyword(s):  
Data Analysis ◽  
Water Injection ◽  
Data Gathering ◽  
Water Flooding ◽  
Success Story ◽  
Full Field ◽  
One Year ◽  
Clastic Reservoirs ◽  
Low Rate

Abstract This paper summarises a ONE development success story of reviving a mature brownfield in South of Oman, Field β, just within ONE year through collaboration between different disciplines, comprehensive data analysis, optimising and recompletion of existing wells. Field β, comprised of multi-stacked clastic reservoirs, was put on stream in 1980s and peaked in early 1990s. Pilot water injection started in 1993 and full field water flooding continued in 1997. After more than 35 years since start of production, one can say the field was already in the tail end of its life. It had been stabilizing at low rate after 25 years and starting to decline further and at some point was one of the potential candidates to be decommissioned. A new FDP (FDP18) for part of the field was delivered in 2018 with the first well drilled at the end of that year. In 2019, despite drilling further wells on the FDP18, production was declining and was at 2018 rate towards the year end. Intensive data analysis and integrated reservoir reviews per reservoir layers were actively performed and new opportunities and data gathering were identified. FDP18 wells from 2019 onwards were then deepened to also acquire log data over deeper than the target reservoirs. Further synergy between asset and exploration teams also instigated in new discoveries including oil in shallower carbonate reservoirs, which were logged and sampled when drilling the FDP18 wells. Declining production, low oil price and COVID-19 crisis that hit 2020 challenged the team to be more resilient and with ONE development mindset between development and WRFM team, also between asset and exploration team, existing long-term closed in and very low productivity wells were utilised to tap these new opportunities. As a result, the field production has been increased by more than double, highest since 10 years ago, with a potential of triple its production rate, all achieved through optimizing and recompletion of existing wells within 1 year, at a very attractive low UTC.

scholarly journals Multiomics data analysis using tensor decomposition based unsupervised feature extraction --Comparison with DIABLO--

2019 ◽  
Author(s):  
Y-h. Taguchi
Keyword(s):  
Feature Extraction ◽  
Data Analysis ◽  
Tensor Decomposition ◽  
Integrated Analysis ◽  
Data Sets ◽  
Data Set ◽  
Advanced Method ◽  
Data Mining Tool ◽  
Mining Tool ◽  
Unsupervised Feature Extraction

AbstractMultiomics data analysis is the central issue of genomics science. In spite of that, there are not well defined methods that can integrate multomics data sets, which are formatted as matrices with different sizes. In this paper, I propose the usage of tensor decomposition based unsupervised feature extraction as a data mining tool for multiomics data set. It can successfully integrate miRNA expression, mRNA expression and proteome, which were used as a demonstration example of DIABLO that is the recently proposed advanced method for the integrated analysis of multiomics data set.

scholarly journals Knowledge guided multi-level network inference

2020 ◽  
Author(s):  
Christoph Ogris ◽  
Yue Hu ◽  
Janine Arloth ◽  
Nikola S. Müller
Keyword(s):  
Data Analysis ◽  
Quantitative Trait ◽  
Network Inference ◽  
Integrated Analysis ◽  
Full Potential ◽  
Omics Data ◽  
Lasso Regression ◽  
Data Set ◽  
Level Data ◽  
Multi Level

AbstractConstantly decreasing costs of high-throughput profiling on many molecular levels generate vast amounts of so-called multi-omics data. Studying one biomedical question on two or more omic levels provides deeper insights into underlying molecular processes or disease pathophysiology. For the majority of multi-omics data projects, the data analysis is performed level-wise, followed by a combined interpretation of results. Few exceptions exist, for example the pairwise integration for quantitative trait analysis. However, the full potential of integrated data analysis is not leveraged yet, presumably due to the complexity of the data and the lacking toolsets. Here we propose a versatile approach, to perform a multi-level integrated analysis: The Knowledge guIded Multi-Omics Network inference approach, KiMONo. KiMONo performs network inference using statistical modeling on top of a powerful knowledge-guided strategy exploiting prior information from biological sources. Within the resulting network, nodes represent features of all input types and edges refer to associations between them, e.g. underlying a disease. Our method infers the network by combining sparse grouped-LASSO regression with a genomic position-confined Biogrid protein-protein interaction prior. In a comprehensive evaluation, we demonstrate that our method is robust to noise and still performs on low-sample size data. Applied to the five-level data set of the publicly available Pan-cancer collection, KiMONO integrated mutation, epigenetics, transcriptomics, proteomics and clinical information, detecting cancer specific omic features. Moreover, we analysed a four-level data set from a major depressive disorder cohort, including genetic, epigenetic, transcriptional and clinical data. Here we demonstrated KiMONo’s analytical power to identify expression quantitative trait methylation sites and loci and show it’s advantage to state-of-the-art methods. Our results show the general applicability to the full spectrum multi-omics data and demonstrating that KiMONo is a powerful approach towards leveraging the full potential of data sets. The method is freely available as an R package (https://github.com/cellmapslab/kimono).

scholarly journals A new three-dimensional effective water-flooding unit model for potential tapping of remained oil in the reservoirs with rhythmic conditions

2021 ◽  
Vol 11 (3) ◽  
pp. 1375-1391
Author(s):  
Weiyao Zhu ◽  
Cunjia Zou ◽  
Jiulong Wang ◽  
Wenchao Liu ◽  
Jiqiang Wang
Keyword(s):  
Water Injection ◽  
Three Dimensional ◽  
Water Flooding ◽  
Two Phase ◽  
Unit Model ◽  
Vertical Heterogeneity ◽  
Remaining Oil ◽  
Water Cut ◽  
Seepage Channel ◽  
Effective Water

AbstractAfter long-term water injection development, most of the oilfields in China have entered the stage of high-water cut, which has reached up to 90%. Due to the strong heterogeneity of the reservoir, more than 50% of the oil remains underground in most oilfields. Therefore, how to predict the distribution and content of remaining oil quickly and accurately in heterogeneous reservoir has become the key of EOR. In this paper, a new effective water-flooding unit model is established based on a three-dimensional flow function, which can characterize the influence of vertical heterogeneity on flow and the streamline distribution. In addition, two shape functions are defined in the model to characterize the oil–water two-phase flow characteristics in an injection-production unit. The results show that the streamline in the lower part of the positive rhythm reservoir is denser, which leads to the formation of dominant seepage channel with ease. However, for the reverse rhythm reservoir, dominant seepage channel forms in the upper part of the reservoir. Besides, for the two types of reservoirs, the greater the permeability difference is, the faster the water cut increases. Furthermore, under the same rhythm condition, the positive rhythm reservoir reaches 90% water cut half a year earlier than the anti-rhythm reservoir. This study provides new insight and guidance for the development of remaining oil in rhythmic reservoirs.

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

Machine learning-assisted production data analysis in liquid-rich Duvernay Formation

2021 ◽  
Vol 200 ◽  
pp. 108377
Author(s):  
Bing Kong ◽  
Zhuoheng Chen ◽  
Shengnan Chen ◽  
Tianjie Qin
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Production Data
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