A Data-Driven Approach to Evaluate Fracturing Practice in Tight Sandstone in Changqing Field

2021 ◽  
Author(s):  
Tao Wu ◽  
Hanzhi Fang ◽  
Hu Sun ◽  
Feifei Zhang ◽  
Xi Wang ◽  
...  
Keyword(s):  
Oil Production ◽  
Gas Production ◽  
Oil Field ◽  
Data Driven ◽  
Data Sets ◽  
Effective Parameters ◽  
Tight Sandstone ◽  
Well Productivity ◽  
Input Parameters ◽  
Data Driven Approach

Abstract Unconventional reservoirs such as shale and tight sandstones that with ultra-low permeability, are becoming increasingly significant in global energy structures (Pejman T, et al., 2017). For these reservoirs, successful hydraulic fracturing is the key to extract the hydrocarbon resources efficiently and economically. However, the intrinsic mechanisms of fracturing growth in the tight formations are still unclear. In practice, fracturing design mainly depends on hypothetical models and previous experience, which leads to difficulties in evaluating the performance of the fracturing jobs. Therefore, an improved method to optimize parameters for fracturing is necessary and beneficial to the industry. In this paper, a data-driven approach is used to evaluate the factors that dominate the production rate from tight sandstone formation in Changqing Field which is the largest oil field in China. In the model, the input parameters are classified into two categories: controllable parameters (e.g. stage numbers, fracturing fluid volume) and uncontrollable parameters (e.g. formation properties), and the output parameter is the accumulated oil production of the wells. Data for more than 100 wells from different formations and zones in Changqing Field are collected for this study. First, a stepwise data mining method is used to identify the correlations between the target parameter and all the available input parameters. Then, a machine learning model is developed to predict the well productivity for a given set of input parameters accurately. The model is validated by using separate data-sets from the same field. An optimize algorithm is combined with the data-driven model to maximize the cumulative oil production for wells by tuning the controllable parameters, which provides the optimized fracturing design. By using the developed model, low productivity wells are identified and new fracturing designs are recommended to improve the well productivity. This paper is useful for understanding the effects of designed fracturing parameters on well productivity in Changqing Oilfield. Furthermore, it can be extended to other unconventional oil fields by training the model with according data sets. The method helps operators to select more effective parameters for fracturing design, and therefore reduce the operation costs for fracturing and improve the oil and gas production.

scholarly journals Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 154
Author(s):  
Marcus Walldén ◽  
Masao Okita ◽  
Fumihiko Ino ◽  
Dimitris Drikakis ◽  
Ioannis Kokkinakis
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Sets ◽  
Output Constraints ◽  
Data Driven Approach ◽  
Scientific Simulations ◽  
Multiple Metrics ◽  
In Transit ◽  
Multiple Compression ◽  
Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

Unlocking By-Passed Oil with Autonomous Inflow Control Devices Through an Integrated Approach

2021 ◽  
Author(s):  
Pawan Agrawal ◽  
Sharifa Yousif ◽  
Ahmed Shokry ◽  
Talha Saqib ◽  
Osama Keshtta ◽  
...  
Keyword(s):  
Continuous Production ◽  
Oil Production ◽  
Gas Production ◽  
Oil Field ◽  
Production Performance ◽  
Fluid Distribution ◽  
Flow Profile ◽  
Horizontal Drain ◽  
Control Devices

Abstract In a giant offshore UAE carbonate oil field, challenges related to advanced maturity, presence of a huge gas-cap and reservoir heterogeneities have impacted production performance. More than 30% of oil producers are closed due to gas front advance and this percentage is increasing with time. The viability of future developments is highly impacted by lower completion design and ways to limit gas breakthrough. Autonomous inflow-control devices (AICD's) are seen as a viable lower completion method to mitigate gas production while allowing oil production, but their effect on pressure drawdown must be carefully accounted for, in a context of particularly high export pressure. A first AICD completion was tested in 2020, after a careful selection amongst high-GOR wells and a diagnosis of underlying gas production mechanisms. The selected pilot is an open-hole horizontal drain closed due to high GOR. Its production profile was investigated through a baseline production log. Several AICD designs were simulated using a nodal analysis model to account for the export pressure. Reservoir simulation was used to evaluate the long-term performance of short-listed scenarios. The integrated process involved all disciplines, from geology, reservoir engineering, petrophysics, to petroleum and completion engineering. In the finally selected design, only the high-permeability heel part of the horizontal drain was covered by AICDs, whereas the rest was completed with pre-perforated liner intervals, separated with swell packers. It was considered that a balance between gas isolation and pressure draw-down reduction had to be found to ensure production viability for such pilot evaluation. Subsequent to the re-completion, the well could be produced at low GOR, and a second production log confirmed the effectiveness of AICDs in isolating free gas production, while enhancing healthy oil production from the deeper part of the drain. Continuous production monitoring, and other flow profile surveys, will complete the evaluation of AICD effectiveness and its adaptability to evolving pressure and fluid distribution within the reservoir. Several lessons will be learnt from this first AICD pilot, particularly related to the criticality of fully integrated subsurface understanding, evaluation, and completion design studies. The use of AICD technology appears promising for retrofit solutions in high-GOR inactive strings, prolonging well life and increasing reserves. Regarding newly drilled wells, dedicated efforts are underway to associate this technology with enhanced reservoir evaluation methods, allowing to directly design the lower completion based on diagnosed reservoir heterogeneities. Reduced export pressure and artificial lift will feature in future field development phases, and offer the flexibility to extend the use of AICD's. The current technology evaluation phases are however crucial in the definition of such technology deployments and the confirmation of their long-term viability.

Data-Driven Approach for Metabolite Relationship Recovery in Biological1H NMR Data Sets Using Iterative Statistical Total Correlation Spectroscopy

2011 ◽  
Vol 83 (6) ◽  
pp. 2075-2082 ◽  
Author(s):  
Caroline J. Sands ◽  
Muireann Coen ◽  
Timothy M. D. Ebbels ◽  
Elaine Holmes ◽  
John C. Lindon ◽  
...  
Keyword(s):  
Correlation Spectroscopy ◽  
Data Driven ◽  
Data Sets ◽  
Nmr Data ◽  
Total Correlation ◽  
Data Driven Approach

A robust data-driven approach identifies four personality types across four large data sets

2018 ◽  
Vol 2 (10) ◽  
pp. 735-742 ◽  
Author(s):  
Martin Gerlach ◽  
Beatrice Farb ◽  
William Revelle ◽  
Luís A. Nunes Amaral
Keyword(s):  
Large Data ◽  
Personality Types ◽  
Large Data Sets ◽  
Data Driven ◽  
Data Sets ◽  
Data Driven Approach

Outlying Sequence Detection in Large Data Sets: A data-driven approach

2014 ◽  
Vol 31 (5) ◽  
pp. 44-56 ◽  
Author(s):  
Ali Tajer ◽  
Venugopal V. Veeravalli ◽  
H. Vincent Poor
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Data Driven ◽  
Data Sets ◽  
Sequence Detection ◽  
Data Driven Approach

scholarly journals A Method for Identifying Environmental Stimuli and Genes Responsible for Genotype-by-Environment Interactions From a Large-Scale Multi-Environment Data Set

2021 ◽  
Vol 12 ◽  
Author(s):  
Akio Onogi ◽  
Daisuke Sekine ◽  
Akito Kaga ◽  
Satoshi Nakano ◽  
Tetsuya Yamada ◽  
...  
Keyword(s):  
Large Scale ◽  
Genetic Correlations ◽  
Data Driven ◽  
Data Sets ◽  
Data Set ◽  
Environmental Stimuli ◽  
Genotype By Environment ◽  
Genome Wide ◽  
Sowing Dates ◽  
Data Driven Approach

It has not been fully understood in real fields what environment stimuli cause the genotype-by-environment (G × E) interactions, when they occur, and what genes react to them. Large-scale multi-environment data sets are attractive data sources for these purposes because they potentially experienced various environmental conditions. Here we developed a data-driven approach termed Environmental Covariate Search Affecting Genetic Correlations (ECGC) to identify environmental stimuli and genes responsible for the G × E interactions from large-scale multi-environment data sets. ECGC was applied to a soybean (Glycine max) data set that consisted of 25,158 records collected at 52 environments. ECGC illustrated what meteorological factors shaped the G × E interactions in six traits including yield, flowering time, and protein content and when these factors were involved in the interactions. For example, it illustrated the relevance of precipitation around sowing dates and hours of sunshine just before maturity to the interactions observed for yield. Moreover, genome-wide association mapping on the sensitivities to the identified stimuli discovered candidate and known genes responsible for the G × E interactions. Our results demonstrate the capability of data-driven approaches to bring novel insights on the G × E interactions observed in fields.

A Data-Driven Approach to Product Usage Context Identification From Online Customer Reviews

2019 ◽  
Vol 141 (12) ◽  
Author(s):  
Dedy Suryadi ◽  
Harrison M. Kim
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Consumer Satisfaction ◽  
Data Driven ◽  
Data Sets ◽  
Product Usage ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Data Driven Approach ◽  
Usage Context

Abstract This paper proposes a data-driven methodology to automatically identify product usage contexts from online customer reviews. Product usage context is one of the factors that affect product design, consumer behavior, and consumer satisfaction. The previous works identify the usage contexts using the survey-based method or subjectively determine them. The proposed methodology, on the other hand, uses machine learning and Natural Language Processing tools to identify and cluster usage contexts from a large volume of customer reviews. Furthermore, aspect sentiment analysis is applied to capture the sentiment toward a particular usage context in a sentence. The methodology is implemented to two data sets of products, i.e., laptop and tablet. The result shows that the methodology is able to capture relevant product usage contexts and cluster bigrams that refer to similar usage context. The aspect sentiment analysis enables the observation of a product’s position with respect to its competitors for a particular usage context. For a product designer, the observation may indicate a requirement to improve the product. It may also indicate a possible market opportunity in a usage context in which most of the current products are perceived negatively by customers. Finally, it is shown that overall rating might not be a strong indicator for representing customer sentiment toward a particular usage context, due to the moderate linear correlation for most of the usage contexts in the case study.

Leveraging a New Well Delivery Methodology for Stellar Drilling Results Steam Injection Project Case Study

2021 ◽  
Author(s):  
Carlos Alejandro Terrones Brand ◽  
Miguel Alejandro Basso Mora ◽  
Rajeswary Kandasamy ◽  
Sergio Comarin ◽  
Felipe Rene Bustos Guevara ◽  
...  
Keyword(s):  
Performance Monitoring ◽  
New Technologies ◽  
Service Providers ◽  
Oil Production ◽  
Steam Injection ◽  
Gas Production ◽  
Oil Field ◽  
Superior Performance ◽  
Stringent Performance

Abstract Mexico has set challenging oil and gas production to meet worldwide demand. In order to deliver promised oil production outputs in this challenging environment, the operator came up with efficient partnerships with key service providers to leverage resources and technical know-how whilst encouraging knowledge transfer and drilling project cost reduction. By working with various service companies, the operator creates a competitive environment where each strives to outperform the other. One such success case is in the "S" field, a heavy oil field producing via steam injection in the South of Mexico. Utilizing a creative design and execution methodology, the "S" project team succeeded to deliver improved project performance over the course of drilling the 14 wells in the campaign. The average well operational time was successfully reduced by 10%, hence maximizing the well construction index to 122 m/day and reducing overall well costs. The main strategy to optimize performance is to re-engineer solutions for profitability such as performing a study to replace OBM by WBM, designing a new wellhead system, collaborating with the rig contractor to reduce flat time activities, redesigning cement properties for losses mitigation, improvement of ROP by merging new technologies and local practices, among others. Complementary to this, the strategy is to prioritize realistic areas of improvement by the development and utilization of a new tool called Best of the Best (BoB), a methodology breaking down all well activities in order to measure its fastest time per well and then aiming to achieve that aggressive goal. Detailed follow up in the field allows to reduce operational times by allowing the wellsite team monitor and suggest new and improved ways of doing a routine task all of which result in lower costs per foot. Utilizing this BoB approach and stringent performance monitoring while drilling (pre-actual-post) activity analysis, allowed superior performance to be achieved. The project reached a 60% improvement on well times from the first well drilled to the best performing well. The best well was drilled in 8.68 days versus a field average of 18 days (217 m/day construction index). This generated 369,000 bbls of earlier oil production, 176 days ahead vs client expectations. Furthermore, in coordination with field staff, lessons learned were captured. But this is not enough since fast and effective communication is required, and the BoB methodology provides the solution to share optimization tricks quickly and effectively between crews, to continue well to well improvement and overall project and field level learning. Improved well delivery results is possible only by aligning the detailed planning and execution follow up in both the wellsite and a remote operations centre which monitored drilling activity in real time from town. This synergy and proactive communication system is also a key factor in the project delivery. This paper will present the results from the first application of the ‘Best of Best' (BoB) methodology in Mexico. This successful application enforces the idea that by coupling re-engineering practices to develop a more creative well design along with stringent performance monitoring; any field performance can be improved to deliver stellar results.

scholarly journals Cognitive Profiles in Parkinson’s Disease and Their Relation to Dementia: A Data-Driven Approach

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Inga Liepelt-Scarfone ◽  
Susanne Gräber ◽  
Monika Fruhmann Berger ◽  
Anne Feseker ◽  
Gülsüm Baysal ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Hierarchical Cluster ◽  
Individual Performance ◽  
Data Driven ◽  
Data Sets ◽  
Cognitive Profiles ◽  
Cluster Analyses ◽  
Data Driven Approach

Parkinson’s disease is characterized by a substantial cognitive heterogeneity, which is apparent in different profiles and levels of severity. To date, a distinct clinical profile for patients with a potential risk of developing dementia still has to be identified. We introduce a data-driven approach to detect different cognitive profiles and stages. Comprehensive neuropsychological data sets from a cohort of 121 Parkinson’s disease patients with and without dementia were explored by a factor analysis to characterize different cognitive domains. Based on the factor scores that represent individual performance in each domain, hierarchical cluster analyses determined whether subgroups of Parkinson’s disease patients show varying cognitive profiles. A six-factor solution accounting for 65.2% of total variance fitted best to our data and revealed high internal consistencies (Cronbach’s alpha coefficients>0.6). The cluster analyses suggested two independent patient clusters with different cognitive profiles. They differed only in severity of cognitive impairment and self-reported limitation of activities of daily living function but not in motor performance, disease duration, or dopaminergic medication. Based on a data-driven approach, divers cognitive profiles were identified, which separated early and more advanced stages of cognitive impairment in Parkinson’s disease without dementia. Importantly, these profiles were independent of motor progression.

scholarly journals A method for identifying environmental stimuli and genes responsible for genotype-by-environment interactions from a large-scale multi-environment data set

2021 ◽  
Author(s):  
Akio Onogi ◽  
Daisuke Sekine ◽  
Akito Kaga ◽  
Satoshi Nakano ◽  
Tetsuya Yamada ◽  
...  
Keyword(s):  
Large Scale ◽  
Genetic Correlations ◽  
Data Driven ◽  
Data Sets ◽  
Data Set ◽  
Environmental Stimuli ◽  
Genotype By Environment ◽  
Genome Wide ◽  
Sowing Dates ◽  
Data Driven Approach

It has not been fully understood in real fields what environment stimuli cause the genotype-by-environment (G x E) interactions, when they occur, and what genes react to them. Large-scale multi-environment data sets are attractive data sources for these purposes because they potentially experienced various environmental conditions. Here we developed a data-driven approach termed Environmental Covariate Search Affecting Genetic Correlations (ECGC) to identify environmental stimuli and genes responsible for the G x E interactions from large-scale multi-environment data sets. ECGC was applied to a soybean (Glycine max) data set that consisted of 25,158 records collected at 52 environments. ECGC illustrated what meteorological factors shaped the G x E interactions in six traits including yield, flowering time, and protein content and when they were involved. For example, it illustrated the relevance of precipitation around sowing dates and hours of sunshine just before maturity to the interactions observed for yield. Moreover, genome-wide association mapping on the sensitivities to the identified stimuli discovered candidate and known genes responsible for the G x E interactions. Our results demonstrate the capability of data-driven approaches to bring novel insights on the G x E interactions observed in fields.

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