Short Term Injection Re-Distribution STIR: Real-Time Waterflood Optimization Technique Using Advanced Data Analytics

2021 ◽  
Author(s):  
Gaurav Modi ◽  
Manu Ujjwal ◽  
Srungeer Simha
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Optimization Technique ◽  
Material Balance ◽  
Oil Production ◽  
Data Driven ◽  
Petroleum Engineering ◽  
Short Term ◽  
Injection Water ◽  
Facility Level

Abstract Short Term Injection Re-distribution (STIR) is a python based real-time WaterFlood optimization technique for brownfield assets that uses advanced data analytics. The objective of this technique is to generate recommendations for injection water re-distribution to maximize oil production at the facility level. Even though this is a data driven technique, it is tightly bounded by Petroleum Engineering principles such as material balance etc. The workflow integrates and analyse short term data (last 3-6 months) at reservoir, wells and facility level. STIR workflow is divided into three modules: Injector-producer connectivity Injector efficiency Injection water optimization First module uses four major data types to estimate the connectivity between each injector-producer pair in the reservoir: Producers data (pressure, WC, GOR, salinity) Faults presence Subsurface distance Perforation similarity – layers and kh Second module uses connectivity and watercut data to establish the injector efficiency. Higher efficiency injectors contribute most to production while poor efficiency injectors contribute to water recycling. Third module has a mathematical optimizer to maximize the oil production by re-distributing the injection water amongst injectors while honoring the constraints at each node (well, facility etc.) of the production system. The STIR workflow has been applied to 6 reservoirs across different assets and an annual increase of 3-7% in oil production is predicted. Each recommendation is verified using an independent source of data and hence, the generated recommendations align very well with the reservoir understanding. The benefits of this technique can be seen in 3-6 months of implementation in terms of increased oil production and better support (pressure increase) to low watercut producers. The inherent flexibility in the workflow allows for easy replication in any Waterflooded Reservoir and works best when the injector well count in the reservoir is relatively high. Geological features are well represented in the workflow which is one of the unique functionalities of this technique. This method also generates producers bean-up and injector stimulation candidates opportunities. This low cost (no CAPEX) technique offers the advantages of conventional petroleum engineering techniques and Data driven approach. This technique provides a great alternative for WaterFlood management in brownfield where performing a reliable conventional analysis is challenging or at times impossible. STIR can be implemented in a reservoir from scratch in 3-6 weeks timeframe.

Real-Time 3D Ultra Deep Directional Electromagnetic LWD Inversions: An Innovative Approach for Geosteering and Geomapping Water Slumping Movement Around Sub-Seismic Fault, Onshore Abu Dhabi

2021 ◽  
Author(s):  
Maniesh Singh ◽  
Parmanand Dhermeshwar Thakur ◽  
Mariam N. M. Al Baloushi ◽  
Haitham Ali Al Saadi ◽  
Maisoon M. Al Mansoori ◽  
...  
Keyword(s):  
Real Time ◽  
Water Movement ◽  
Oil Production ◽  
Carbonate Reservoir ◽  
Advanced Technology ◽  
Fluid Distribution ◽  
3D Inversion ◽  
The Real ◽  
Reservoir Models ◽  
Injection Water

Abstract An Ultra-Deep Directional Electromagnetic LWD Resistivity (UDDE) tool was deployed in a mature Lower Cretaceous carbonate reservoir to map injection water movement. These thick carbonate reservoirs experience injection water preferentially travelling laterally at the top of the reservoir. The water held above oil by negative capillary forces slumps quickly, leading to increasing water cut, eventually killing the natural lift horizontal producing well. Real time 3D and 1D inversions provided important accurate mapping of the non-uniform water fronts and reservoir boundaries, providing insights into reservoir architecture and water movement. The candidate well is located in an area of significant uncertainty regarding fluid distribution and structural elements like sub-seismic faults etc. Pre-well 1D inversion results indicated that the water slumping front away from wellbore can be mapped within a vertical radius of 60-100 ft TVD. However, 1D inversion is not accurate where steeply dipping or discontinuous formations exist due to the presence of faults and is expected to impact well placement, mapping water fronts / formation boundaries and long-term oil recovery. Therefore in the real time, full 3D and 1D inversions of the Ultra-Deep EM data were run to provide high quality reservoir imaging in this complex geometrical setting and deliver improved reservoir fluid distribution and structure mapping. The pre-well inversion modeling optimized the frequency and transmitter-receiver spacing of the UDDE tool. The bottom hole assembly (BHA) configuration also included conventional LWD tools such as Neutron-Density, propagation Resistivity and Gamma Ray. Multiple 3D inversion datasets were processed in real-time using different depths of inversion ranging from 50 ft up to 120 ft depth. The 3D inversion results during the real-time drilling operation detected the non-uniform waterfront boundaries and water slumping up to 80 ft TVD above the wellbore using a slimhole (4¾″) tool. An interpreted sub-seismic down-thrown fault was mapped which controlled the non-uniform slumping fluid distribution, causing the water front to approach closest to the wellbore in this location. This suggests that the fault zone is open and provides a degree of increased permeability around the plane of the fault. The real-time 3D inversion, 1D shallow and 1D deep inversion results showed comparable structural imaging despite being inverted independently of each other. These results permitted updates to the static / dynamic reservoir models and an optimization of the completion design, to delay the water influx and thereby sustain oil production for a longer period of time. Field wide implementation of the UDDE tool and its advanced technology with improved 1D and 3D inversion results will enhance the quality of realtime geosteering, mapping and updating of reservoir models which have challenging water slumping fronts and structural variations. This will enable improvment in well locations, their spacing and finally allowing the proactive design of smart completions for enhanced oil production and improved recovery factors.

Application of Advanced Data Analytics for Gas Reservoirs and Wells Management

2021 ◽  
Author(s):  
Ashwin Srinivasan ◽  
Gaurav Modi ◽  
Rahul Agrawal ◽  
Viren Kumar
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Production Management ◽  
Petroleum Industry ◽  
Oil Field ◽  
Petroleum Engineering ◽  
Multiple Sources ◽  
Opportunity Identification ◽  
Diagnostic Plots ◽  
Time Required

Abstract Objectives/Scope The amount of time and effort required to access and integrate Subsurface data from multiple sources is significant. Using Advanced Data Analytics, mainly python, an integrated subsurface dashboard titled Hybrid Integrated Visualization Environment (HIVE) was created using Spotfire to empower the integrated Exploration, Development and Well Reservoir and Facilities Management (WRFM) subsurface teams in: Professionalizing data and knowledge management to have "one" version of the truth. Data consolidation and preparation to avoid repetitive manual work & Enhancing opportunity identification to optimize production and value Methods, procedure, process The approach of subsurface data integration can be broken down into 4 major steps, namely: Step 1: Python programming was used to pre-process, restructure and create unified data frames. Use of python significantly reduces the time required to pre-process a diverse number of subsurface data sources consisting of static, dynamic reservoir models, log data, historical production & pressure data and wells & completion data to name a few. Step 2: - Standard diagnostic industry recognized diagnostic plots were automated using advanced analytic techniques in HIVE with the help of unified data frames. Step 3: HIVE was created to link various internal corporate data stores like pressure, temperature, rate data from PI System (stores real time measured data), Energy Components (EC) and Oil Field Manager (OFM) in real time. This was done to ensure that data from various petroleum engineering disciplines could now be visualized and analyzed in a structured manner to make integrated business decisions. Step 4: One of the key objectives of pursuing this initiative was to ensure that subsurface professionals in Shell Trinidad and Tobago were trained and upskilled in the use of python as well visualization tools like Spotfire and Power BI to ensure the maintenance and improvement of HIVE going forward. Results, Observations, Conclusions The development of HIVE has made it easier and more efficient to access and visualize subsurface data, which was extremely time consuming earlier while using older conventional techniques. Standard diagnostic plots and visuals were developed and are now used to drive integrated decision making, with key focus being water and sand production management from a production management perspective. Consequently, HIVE also drives enhanced integration between disciplines (Petrophysics, Petroleum Geology, Production Technology, Reservoir Engineering and Production operations) and departments (Developments, Upstream and Exploration). Novel/Additive Information The petroleum industry has started to embrace the application of advanced data analytics in our day-to-day work. A successful application of these techniques results in transforming the ways of working by increasing efficiency, transparency and integration among teams.

scholarly journals Welcome to a New Generation of Entertainment: Amazon Web Services and the Normalization of Big Data Analytics and RFID Tracking

2019 ◽  
Vol 17 (1/2) ◽  
pp. 169-175 ◽  
Author(s):  
Justin Joseph Grandinetti
Keyword(s):  
Big Data ◽  
Web Services ◽  
Real Time ◽  
Radio Frequency Identification ◽  
Spatial Data ◽  
Data Analytics ◽  
National Football League ◽  
Big Data Analytics ◽  
Data Driven ◽  
Amazon Web Services

The 2017 partnership between the National Football League (NFL) and Amazon Web Services (AWS) promises novel forms of cutting-edge real-time statistical analysis through the use of both radio frequency identification (RFID) chips and Amazon’s cloud-based machine learning and data-analytics tools. This use of RFID is heralded for its possibilities: for broadcasters, who are now capable of providing more thorough analysis; for fans, who can experience the game on a deeper analytical level using the NFL’s Next Gen Stats; and for coaches, who can capitalize on data-driven pattern recognition to gain a statistical edge over their competitors in real-time. In this paper, we respond to calls for further examination of the discursive positionings of RFID and big data technologies (Frith 2015; Kitchin and Dodge 2011). Specifically, this synthesis of RFID and cloud computing infrastructure via corporate partnership provides an alternative discursive positioning of two technologies that are often part of asymmetrical relations of power (Andrejevic 2014). Consequently, it is critical to examine the efforts of Amazon and the NFL to normalize pervasive spatial data collection and analytics to a mass audience by presenting these surveillance technologies as helpful tools for accessing new forms of data-driven knowing and analysis.

scholarly journals Real-time Big Data Analytics Framework with Data Blending Approach for Multiple Data sources in Smart City Applications

2020 ◽  
Vol 21 (4) ◽  
pp. 611-623
Author(s):  
Manjunatha S ◽  
Annappa B
Keyword(s):  
Real Time ◽  
Smart City ◽  
Data Analytics ◽  
Public Safety ◽  
Big Data Analytics ◽  
Data Sources ◽  
Data Driven ◽  
The Public ◽  
Multiple Data Sources ◽  
Multiple Data

Advancement in Information Communication Technology (ICT) and the Internet of Things (IoT) has to lead tothe continuous generation of a large amount of data. Smart city projects are being implemented in various parts of the world where analysis of public data helps in providing a better quality of life. Data analytics plays a vital role in many such data-driven applications. Real-time analytics for finding valuable insights at the right time using smart city data is crucial in making appropriate decisions for city administration. It is essential to use multiple data sources as input for the analysis to achieve better and more accurate data-driven solutions. It helps in finding more accurate solutions and making appropriate decisions. Public safety is one of the major concerns in any smart city project in which real-time analytics is much useful in the early detection of valuable data patterns. It is crucial to find early predictions of crime-related incidents and generating emergency alerts for making appropriate decisions to provide security to the people and safety of the city infrastructure. This paper discusses the proposed real-time big data analytics framework with data blending approach using multiple data sources for smart city applications. Analytics using multiple data sources for a specific data-driven solution helps in finding more data patterns, which in turn increases the accuracy of analytics results. The data preprocessing phase is a challenging task in data analytics when data being ingested continuously in real-time into the analytics system. The proposed system helps in the preprocessing of real-time data with data blending of multiple data sources used in the analytics. The proposed framework is beneficial when data from multiple sources are ingested in real-time as input data and is also flexible to use any additional data source of interest. The experimental work carried out with the proposed framework using multiple data sources to find the crime-related insights in real-time helps the public safety solutions in the smart city. The experimental outcome shows that there is a significant increase in the number of identified useful data patterns as the number of data sources increases. A real-time based emergency alert system to help the public safety solution is implementedusing a machine learning-based classification algorithm with the proposed framework. The experiment is carried out with different classification algorithms, and the results show that Naive Bayes classification  performs better in generating emergency alerts.

Ultra-Deep LWD Electromagnetic Directional Resistivity for Waterflood Mapping – A Game Changer

2021 ◽  
Author(s):  
Parmanand Thakur ◽  
Maniesh Singh ◽  
Saif Al Arfi ◽  
Mohamed Al Gohary ◽  
Mariam Al Baloushi ◽  
...  
Keyword(s):  
Real Time ◽  
Structural Changes ◽  
Water Movement ◽  
Oil Production ◽  
Optimum Operating ◽  
Reservoir Water ◽  
Moderate Amount ◽  
Seismic Fault ◽  
Injection Water ◽  
Fluid Characterization

Abstract Abu Dhabi's thick Lower Cretaceous carbonate reservoirs experience injection water overriding oil. The water is held above the oil by negative capillary pressure until a horizontal borehole placed at the reservoir base creates a small pressure drawdown. This causes the water above to slump unpredictably towards the horizontal producer, increasing water cut and eventually killing the well under natural lift after a moderate amount of oil production. Water slumping is difficult to forecast using the reservoir model. This paper showcases the successful deployment of an ultra-deep electromagnetic directional resistivity (UDDE) instrument to map injection water movement. The UDDE instrument selected for the 6-in. horizontal hole was a 4¾-in. OD multifrequency tool with configurable transmitter-to-receiver spacings. Pre-well modeling using hybrid deterministic 1D resistivity inversions was conducted for the candidate well to investigate the resistivity tool's ability to identify water slumping at distances 60-100 ft TVD above the planned well trajectory. The inversions aided the selection of optimum operating frequencies, transmitter-to-receiver spacings and BHA configuration. During operations, multiple 1D and 3D inversions were run in the cloud real time during drilling to provide simultaneous deep and shallow resistivity inversions for early identification of the water fronts and structural changes, and near wellbore changes to geosteer and maximize reservoir contact in the complex layered reservoir. Real-time 1D and 3D deep inversion results indicated the resistivity tool had a depth of reliable waterflood detection of more than 80 ft. While drilling, an interpreted subseismic fault was encountered which appeared to influence how water moved in the reservoir. Water slumped closest through the sub-seismic fault towards the well path. Past the fault, the waterfront receded upwards away from the well bore. The data proved useful for updating the static model, providing a snapshot of water flood areas, reservoir tops and faults with throw, helping to optimize the completion design to defer water production and enhance oil production. Furthermore, it captured resistivities of target, underlying and overlying reservoirs to integrate with other geology and geophysics data for better reservoir and fluid characterization near the drilled area. The positive results of this case study encouraged field-wide implementation of this technology for waterflood mapping. The information provided allowed petroleum engineers to adjust the completion design to delay water breakthrough. This proactive approach to waterflood field management improves cumulative oil production and recovery factors according to mechanistic models which have been built and tested.

scholarly journals DeepCOVID: An Operational Deep Learning-driven Framework for Explainable Real-time COVID-19 Forecasting

2020 ◽  
Author(s):  
Alexander Rodríguez ◽  
Anika Tabassum ◽  
Jiaming Cui ◽  
Jiajia Xie ◽  
Javen Ho ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Noisy Data ◽  
Sparse Data ◽  
Heterogeneous Data ◽  
Exploratory Analysis ◽  
Data Driven ◽  
Short Term ◽  
Learning Framework ◽  
Disease Exposure

AbstractHow do we forecast an emerging pandemic in real time in a purely data-driven manner? How to leverage rich heterogeneous data based on various signals such as mobility, testing, and/or disease exposure for forecasting? How to handle noisy data and generate uncertainties in the forecast? In this paper, we present DeepCovid, an operational deep learning framework designed for real-time COVID-19 forecasting. Deep-Covid works well with sparse data and can handle noisy heterogeneous data signals by propagating the uncertainty from the data in a principled manner resulting in meaningful uncertainties in the forecast. The deployed framework also consists of modules for both real-time and retrospective exploratory analysis to enable interpretation of the forecasts. Results from real-time predictions (featured on the CDC website and FiveThirtyEight.com) since April 2020 indicates that our approach is competitive among the methods in the COVID-19 Forecast Hub, especially for short-term predictions.

scholarly journals Best Practice Operation of Reversible Express Lanes for the Kennedy Expressway

2021 ◽  
Author(s):  
Abolfazl Mohammadian ◽  
◽  
Amir Bahador Parsa ◽  
Homa Taghipour ◽  
Amir Davatgari ◽  
...  
Keyword(s):  
Real Time ◽  
Best Practice ◽  
Central City ◽  
Optimization Technique ◽  
Congestion Management ◽  
Optimization Techniques ◽  
Data Driven ◽  
Special Focus ◽  
Surveillance Camera ◽  
Real Time Traffic

Reversible lanes in Chicago’s Kennedy Expressway are an available infrastructure that can significantly improve traffic performance; however, a special focus on congestion management is required to improve their operation. This research project aims to evaluate and improve the operation of reversible lanes in the Kennedy Expressway. The Kennedy Expressway is a nearly 18-mile-long freeway in Chicago, Illinois, that connects in the southeast to northwest direction between the West Loop and O’Hare International Airport. There are two approximately 8-mile reversible lanes in the Kennedy Expressway’s median, where I-94 merges into I-90, and there are three entrance gates in each direction of this corridor. The purpose of the reversible lanes is to help the congested direction of the Kennedy Expressway increase its traffic flow and decrease the delay in the whole corridor. Currently, experts in a control location switch the direction of the reversible lanes two to three times per day by observing real-time traffic conditions captured by a traffic surveillance camera. In general, inbound gates are opened and outbound gates are closed around midnight because morning traffic is usually heavier toward the central city neighborhoods. In contrast, evening peak-hour traffic is usually heavier toward the outbound direction, so the direction of the reversible lanes is switched from inbound to outbound around noon. This study evaluates the Kennedy Expressway’s current reversing operation. Different indices are generated for the corridor to measure the reversible lanes’ performance, and a data-driven approach is selected to find the best time to start the operation. Subsequently, real-time and offline instruction for the operation of the reversible lanes is provided through employing deep learning and statistical techniques. In addition, an offline timetable is also provided through an optimization technique. Eventually, integration of the data-driven and optimization techniques results in the best practice operation of the reversible lanes.

scholarly journals Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis

2020 ◽  
Author(s):  
Tanujit Chakraborty ◽  
Indrajit Ghosh
Keyword(s):  
Risk Assessment ◽  
Real Time ◽  
Moving Average ◽  
Hybrid Approach ◽  
Regression Tree ◽  
Case Fatality ◽  
Warning System ◽  
Data Driven ◽  
Short Term ◽  
The Future

AbstractThe coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern affecting 201 countries and territories around the globe. As of April 4, 2020, it has caused a pandemic outbreak with more than 11,16,643 confirmed infections and more than 59,170 reported deaths worldwide. The main focus of this paper is two-fold: (a) generating short term (real-time) forecasts of the future COVID-19 cases for multiple countries; (b) risk assessment (in terms of case fatality rate) of the novel COVID-19 for some profoundly affected countries by finding various important demographic characteristics of the countries along with some disease characteristics. To solve the first problem, we presented a hybrid approach based on autoregressive integrated moving average model and Wavelet-based forecasting model that can generate short-term (ten days ahead) forecasts of the number of daily confirmed cases for Canada, France, India, South Korea, and the UK. The predictions of the future outbreak for different countries will be useful for the effective allocation of health care resources and will act as an early-warning system for government policymakers. In the second problem, we applied an optimal regression tree algorithm to find essential causal variables that significantly affect the case fatality rates for different countries. This data-driven analysis will necessarily provide deep insights into the study of early risk assessments for 50 immensely affected countries.

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