Data-Driven Well Pad Development Performance Review: Focus on the Role of Liner Design on Well Performance

2021 ◽  
Author(s):  
Hossein Izadi ◽  
Morteza Roostaei ◽  
Mahdi Mahmoudi ◽  
Seyed Abolhassan Hosseini ◽  
Mohammad Soroush ◽  
...  
Keyword(s):  
Oil Production ◽  
Relative Performance ◽  
Data Driven ◽  
Thickness Variation ◽  
Well Performance ◽  
Drainage Volume ◽  
Well Completion ◽  
Flow Control Devices ◽  
Using Data

Abstract Steam Assisted Gravity Drainage (SAGD) is the dominant in-situ method for oil production in Western Canada. The current study analyzed the relative performance of various well-completion practices using data from 4,000 well pairs that were drilled over a decade. The data analysis provided a unique opportunity to find best operating practices. The scope of this paper is to review the performance of major thermal projects in Canada and investigating the effect of liner design and Flow Control Devices (FCDs) on well pair performance and development. Cumulative oil production and cumulative steam oil ratio (cSOR) were used as the key metrics in comparing the well performance in a SAGD operation. However, to compare different pads and different projects, it was critical to normalize the data with geological variation, well length, well spacing, and with consideration to the well failure rate, remedial completion and re-drills. In this paper we review seven thermal projects of four key operators with almost 3,500 wells and 1,200 well pairs in operation as early as 1996. All geoscience, and production/injection data have been extracted from public databases and utilized to develop a data-driven model. The reservoir thickness variation for each well was determined using available geoscience data, and through the development of a geological model based on the available core data and well logs. The model was used to define the drainage volume for each well pair, which in turn was used to assign a geological ranking to the well. The cumulative oil production and cSOR were then normalized with the geological ranking and the size of the net drainage volume. The number of well pairs in each pad and the cumulative pad production were normalized against the number of days in production and their relative decline, which allowed for comparison between pads within the same project, as well as pads from other projects. The cumulative production of the active pads in each project was used to compare the relative performance of different projects. Also, we separated the projects and wells based on their use of FCDs in the producer and injector to compare the relative performance of each technology in the field. This paper is the initial phase of the study on the role of completion design on relative well and well pad performance. The results will help completion and production engineers to better understand the well pair and pad relative performance and how to normalize the oil production data against geological variation to compare performance.

Challenges from Well Shut-in Amid the Oil Downturn: Long Term Impacts on Near Wellbore Skin Buildup and Sand Control

2021 ◽  
Author(s):  
Mohammad Soroush ◽  
Mahdi Mahmoudi ◽  
Morteza Roostaei ◽  
Hossein Izadi ◽  
Seyed Abolhassan Hosseini ◽  
...  
Keyword(s):  
Hot Spot ◽  
Oil Production ◽  
Well Performance ◽  
Well Completion ◽  
Flow Control Devices ◽  
Sand Control ◽  
Control Devices ◽  
Inflow Problem ◽  
The Impact

Abstract In wake of the biggest oil crash in history triggered by the COVID-19 pandemic; Western Canada in- situ production is under tremendous price pressure. Therefore, the operators may consider shut in the wells. Current investigation offers an insight into the effect of near-wellbore skin buildup because of such shut-in. A series of simulation studies was performed to quantitatively address the impact of well shut-in on the long-term performance of well, in particular on key performance indicators of the well including cumulative steam to oil ratio and cumulative oil production. The long-term shut-in contributes to three main modes of plugging: (1) near-wellbore pore plugging by clays and fines, (2) scaling, and (3) chemical consolidation induced by corrosion. A series of carefully designed simulations was also utilized to understand the potential of skin buildup in the near-wellbore region and within different sand control devices. The simulation results showed a higher sensitivity of well performance to shut-in for the wells in the initial stage of SAGD production. If the well is shut in during the first years, the total reduction in cumulative oil production is much higher compared to a well which is shut-in during late SAGD production life. As the induced skin due to shut-in increases, the ultimate cumulative oil production drops whose magnitude depends on well completion designs. The highest effect on the cumulative oil production is in the case of completion designs with flow control devices (liner deployed and tubing deployed completions). Therefore, wellbore hydraulics and completion design play key roles in the maintenance of uniform inflow profile, and the skin buildup due to shut-in poses a high risk of inflow problem and increases the risk of hot-spot development and steam breakthrough. This investigation offers a new understanding concerning the effect of shut-in and wellbore skin buildup on SAGD operation. It helps production and completion engineers to better understand and select candidate wells for shut-in and subsequently to minimize the skin buildup in wells.

Oil-Rate Forecast by Inferring Fractional-Flow Models From Field Data With Koval Method Combined With the Capacitance/Resistance Model

2015 ◽  
Vol 18 (04) ◽  
pp. 534-553 ◽  
Author(s):  
Fei Cao ◽  
Haishan Luo ◽  
Larry W. Lake
Keyword(s):  
Field Data ◽  
Oil Production ◽  
Data Driven ◽  
Analytical Models ◽  
Fractional Flow ◽  
Physical Behavior ◽  
Flow Models ◽  
Drainage Volume ◽  
Resistance Model ◽  
Water Cut

Summary Many empirical and analytical models were developed to forecast oil production. Empirical models (including data-driven models) can, for example, find correlations between oil cut and production, but they lack explicit knowledge of the physical behavior. Classic analytical models are loyal to reservoir physics. Nevertheless, they often require estimation of water saturation as a function of time, which is difficult to obtain for multiwell reservoirs. It is desirable to combine advantages of both empirical and analytical models and develop a physical-model-based method that uses field data to infer oil rate. In this paper, we propose to infer fractional-flow models from field data by use of the Koval (1963) theory. We inversely solved the Koval fractional-flow equation to obtain a relationship between water cut and dimensionless time. By history matching field water-cut data, two model parameters, the Koval factor and the producer-drainage volume, are estimated. Nevertheless, it is challenging to use the Koval approach as a predictive model directly because the injection contribution into each producer in a future-time horizon must be evaluated first. To address this issue, we combine the Koval approach with the capacitance/resistance model (CRM), which characterizes the injector/producer connectivities and response time. The material balance of fluids is established in a producer-based drainage volume to consider the contributions from nearby injectors and the time lag in production caused by reservoir/fluids compressibility. A regression approach is simultaneously advanced to minimize the model error. Because of robustly integrating the reservoir physical behavior and the data-driven approach, the combination of the Koval theory and the CRM can result in a synergy that leads to accurate oil-rate predictions. We validated this integrated method in synthetic homogeneous and heterogeneous reservoirs to test its reliability, and further applied it to a field case in western Venezuela. Case studies demonstrate that one can use this integrated model as a real-time tool to characterize interwell connection and to predict future oil production accurately.

scholarly journals COVID-19 Risk Minimization Decision Making Strategy Using Data-Driven Model

2021 ◽  
Vol 3 (1) ◽  
pp. 57-66
Author(s):  
Akey Sungheetha
Keyword(s):  
Decision Making ◽  
Data Driven ◽  
Risk Minimization ◽  
Proposed Model ◽  
Epidemic Intelligence ◽  
Spread Dynamics ◽  
Using Data ◽  
The Impact ◽  
Global Strategies

In order to establish social resilient and sustainable cities during the pandemic outbreak, it is essential to forecast the epidemic trends and trace infection by means of data-driven solution addressing the requirements of local operational defense applications and global strategies. The smartphone based Digital Proximity Tracing Technology (DPTT) has obtained a great deal of interest with the ongoing COVID-19 pandemic in terms of mitigation, containing and monitoring with the population acceptance insights and effectiveness of the function. The DPTTs and Data-Driven Epidemic Intelligence Strategies (DDEIS) are compared in this paper to identify the shortcomings and propose a novel solution to overcome them. In terms of epidemic resurgence risk minimization, guaranteeing public health safety and quick return of cities to normalcy, a social as well as technological solution may be provided by incorporating the key features of DDEIS. The role of human behavior is taken into consideration while assessing its limitations and benefits for policy making as well as individual decision making. The epidemiological model of SEIR (Susceptible–Exposed–Infectious–Recovered) provides preliminary data for the preferences of users in a DPTT. The impact of the proposed model on the spread dynamics of Covid-19 is evaluated and the results are presented.

Data is different, and that’s why the world needs a new approach to governing cross-border data flows

2019 ◽  
Vol 21 (5) ◽  
pp. 441-460 ◽  
Author(s):  
Susan Ariel Aaronson
Keyword(s):  
Market Access ◽  
Current Approach ◽  
Data Driven ◽  
New Approach ◽  
Content Type ◽  
Goods And Services ◽  
Cross Border ◽  
Data Flows ◽  
Using Data

Purpose Companies, governments and individuals are using data to create new services such as apps, artificial intelligence (AI) and the Internet of Things (IoT). These data-driven services rely on large pools of data and a relatively unhindered flow of data across borders (few market access or governance barriers). The current approach to governing cross-border data flows through trade agreements and has not led to binding, universal or interoperable rules governing the use of data. The purpose of this article is to explain the new role of data in trade and to explain why data in trade is different from trade in other goods and services. We then suggest a new approach at the national and international levels. Design/methodology/approach The author uses a mixed methods approach to examine what the literature says about data as a traded good and or service, examines metaphors regarding the role of data in the economy, and then examines whether or not data is really “traded.” Findings Many countries do not know how to regulate data driven services. There is no consensus on what the appropriate regulatory environment looks like, nor is there a consensus on what are the barriers to cross-border data flows and what constitutes legitimate domestic regulation. Originality/value This is the first article to explain both the unique nature of data and the ineffectiveness of the trade system to address that distinctiveness.

INOVASI PEMBELAJARAN AKIDAH AKHLAK MENGGUNAKAN METODE ROLE PLAY DALAM MENINGKATKAN PRESTASI BELAJAR SISWA

2018 ◽  
Vol 2 (1) ◽  
pp. 52-63
Author(s):  
Ansori Ansori
Keyword(s):  
Data Reduction ◽  
Role Play ◽  
Learning Goals ◽  
New Paradigm ◽  
Learning Materials ◽  
Analysis Technique ◽  
Flexible Schedule ◽  
Using Data ◽  
Real Action

The use of various methods will greatly help students in achieving learning goals. As role play method is one way mastery of learning materials through the development of imagination and appreciation of students on learning materials. Data collection techniques in this study are observation, interviews, and documentation. To analyze the data in this research using data analysis technique of Miles and Huberman model that is data reduction (Data Reduction), data presentation (Data Display) and conclusion (Conclution Drawing / verification) The findings in this research is innovation of role play method can change paradigm to the new paradigm so that the role of the teacher is more as a facilitator, counselor, consultant, and comrade study Flexible schedule, open as needed Learning directed by students themselves Problem-based, project, real world, real action, and reflection Design and investigation. Computers as tools, and dynamic media presentations.

Development of a Pediatric Early Warning System Using Data-Driven Vital Signs

PEDIATRICS ◽  
2016 ◽  
Vol 137 (Supplement 3) ◽  
pp. 256A-256A
Author(s):  
Catherine Ross ◽  
Iliana Harrysson ◽  
Lynda Knight ◽  
Veena Goel ◽  
Sarah Poole ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Vital Signs ◽  
Warning System ◽  
Data Driven ◽  
Using Data

The role of corporate investment in social responsible practices in the promotion of employees’ organizational citizenship behaviour

2014 ◽  
Author(s):  
Ana Patrícia Duarte ◽  
José Gonçalves das Neves
Keyword(s):  
Job Satisfaction ◽  
Organizational Citizenship ◽  
Economic Uncertainty ◽  
Socially Responsible ◽  
Organizational Survival ◽  
Current Scenario ◽  
Reducing Costs ◽  
Using Data ◽  
Airline Company

In the current scenario of economic uncertainty, where many organizations struggle harder for reducing costs while improving their competitiveness, employees’ organizational citizenship behaviours might have an enhanced importance for organizational survival and success. A model proposing that corporate engagement in internal socially responsible practices enhances employees’ job satisfaction and consequently increases employees’ extra-role behaviours was tested. Using data obtained from a sample of employees from an airline company (n=133), the model was examined and supported. We have therefore concluded that organizations may foster employees’ extra-role behaviours by investing in corporate socially responsible practices that signal corporate concern with employees and promote job satisfaction.

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