Intuition suggests that, if a little bit of information is valuable, then a lot must be more so. But is it? It’s not the data itself, but the ability to put it towards some useful task that matters. One of the most concrete ways in which big data can be brought to bear on the everyday is through predictive Analytics
Analytics
Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability.
Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability.
Read this Term, the science of using facts from the past to analyse the present and predict the future.
In the oilfield, a simple yet powerful way of making use of data and predictive analytics is for automatic surveillance by exception. By learning from the past, it is possible to predict what should be happening today and compare the prediction to what is actually going on. If the two do not match, then a problem has been identified and a solution found. Addressing problems early is one of the cheapest ways to boost production.
The need for improved prediction
Wells that are big producers are typically watched by many eyes. Extensive instrumentation records pressures, temperatures, volumes and equipment performance parameters at regular intervals, which can be as often as every minute. This enables a large staff to quickly respond to unusual readings.
However, engineers and instruments are expensive and, in oilfields with multiple small wells, a single person is often responsible for the production of a hundred wells for which they receive information only once daily. Horror stories abound in which wells failed to produce at expected rates before an operator caught on and the situation was remedied.
Yet, if data can be used to effectively provide a metric against which to measure current production, a simple, inexpensive system that requires no instrumentation other than the production monitoring, well testing and production allocations can potentially save millions of dollars in deferred or lost production.
Having a grounded metric for production is an effective means of computing automatic warning alarms and of correlating production alarms to geographical location, geology and particular operators.
It’s not just engineers who need improved surveillance based on data that is already being collected and generally available. There are many other parties, like for instance non-operating partners, royalty owners and lenders, who also have an interest in making sure that production does not drop below reasonable levels. Yet, they have limited access to data other than production records.
The key to improving surveillance is improving prediction because the more accurately one can predict what production should be, the easier it is to spot deviation from the ideal.
How to achieve improved surveillance
The best tool to achieve improved surveillance is a library of accurate, probabilistic and unbiased oil and gas production forecasts for both individual and aggregate wells by reservoir, operator, basin or region. These forecasts should provide the full range of future production possibilities by combining map and production data with long-term production forecasts generated in an automated fashion and updated monthly.
But how can you forecast future volumes accurately to build your library? You will need a technology solution that gathers and interprets monthly oil, gas and water allocated volumes and records of working days (if available).
Such technology should consist of a physical part explaining data in terms that are physically plausible, and a statistical part explaining normal deviations from physical behaviour such as stimulations, shut-ins and bad data. Once the samples are drawn, they can be pushed forward to make a probabilistic forecast. Statistics are then computed over the samples. Rigorous calibration with blind testing are needed to ensure that the statistics of the samples match real data.
Because of its statistical nature, the solution can be used as an easily understood and controllable tool for surveillance. It can be used to set thresholds of a very specific nature – namely, the user can ask to be warned when production goes below a particular p-value and they can expect to receive warnings from each well under surveillance. The number of alarms can be reduced by using filters that reflect other factors such as changes in water cut, known shut-ins for maintenance and the like.
In summary, technology that helps you understand data and forecast events can provide constant watch over your operation and trigger alarms and alerts when defined events occur, allowing fewer staff to effectively manage larger portfolios and more assets.
As the volume of data from across organisations continues to grow, it becomes harder and harder to monitor KPIs and operating conditions through traditional dashboards relying on manual views and analysis. The sensible approach is automated exception management through user defined rules and conditions that monitor data and initiate alerts.
Conclusion
Knowing what to expect is ultimately the key to surveillance. Accurate forecasts facilitate the decision-making process for a variety of users, from financial investors looking to make sound investment to state officials wanting to make better resource-allocation decisions, from production engineers wanting to compare their wells’ performance with their competitors’ performance to upstream executives heavily involved in acquisitions and divestitures.
Having a better understanding of how wells are going to perform in the future is a powerful tool that represents a major change for the oil and gas data information sector. Better understanding always Leads
Leads
Leads or lead generation are an essential component of marketing and powerful tool by brokers. In its simplest form, leads can be defined as the outreach of customer interest or enquiry into products or services, most often associated with brokerages.These can be created for purposes such as list building, e-newsletter list acquisition, or for sales leads. Amongst marketers, such lists are one of their most important assets and instrumental to sales.There are a variety of methods for generating leads that traditionally fall under the mantle of advertising. However, this may also include non-paid sources such as organic search engine results or referrals from existing customersHow Are Leads Generated?In the FX space, nearly every brokerage has their own list of leads. How exactly these are generated varies to some extent. Most come from a composite of sources or activities.Specific parameters on the Internet such as personal referrals, telephone calls, or even conference attendance either by the company or telemarketers, through advertisements are the most common examples of this.Indeed, content marketing, search engine, and events are all effective ways in bolstering leads over time and account for the highest concentration of lead generation.Leads are also a powerful took by marketers to pursue new clients. This can involve customer relationship management (CRM) technology or follow ups in the form of contacting.The goal of these contacts is the conversion into a client. Simply obtaining a list of leads does not always correlate to business. This is where sales, follow ups, or other methods come into play.
Leads or lead generation are an essential component of marketing and powerful tool by brokers. In its simplest form, leads can be defined as the outreach of customer interest or enquiry into products or services, most often associated with brokerages.These can be created for purposes such as list building, e-newsletter list acquisition, or for sales leads. Amongst marketers, such lists are one of their most important assets and instrumental to sales.There are a variety of methods for generating leads that traditionally fall under the mantle of advertising. However, this may also include non-paid sources such as organic search engine results or referrals from existing customersHow Are Leads Generated?In the FX space, nearly every brokerage has their own list of leads. How exactly these are generated varies to some extent. Most come from a composite of sources or activities.Specific parameters on the Internet such as personal referrals, telephone calls, or even conference attendance either by the company or telemarketers, through advertisements are the most common examples of this.Indeed, content marketing, search engine, and events are all effective ways in bolstering leads over time and account for the highest concentration of lead generation.Leads are also a powerful took by marketers to pursue new clients. This can involve customer relationship management (CRM) technology or follow ups in the form of contacting.The goal of these contacts is the conversion into a client. Simply obtaining a list of leads does not always correlate to business. This is where sales, follow ups, or other methods come into play.
Read this Term to better decisions.
This article was written by Grant Eggleton, Vice President, Global Production Solutions at P2 Energy Solutions.
Intuition suggests that, if a little bit of information is valuable, then a lot must be more so. But is it? It’s not the data itself, but the ability to put it towards some useful task that matters. One of the most concrete ways in which big data can be brought to bear on the everyday is through predictive Analytics
Analytics
Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability.
Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability.
Read this Term, the science of using facts from the past to analyse the present and predict the future.
In the oilfield, a simple yet powerful way of making use of data and predictive analytics is for automatic surveillance by exception. By learning from the past, it is possible to predict what should be happening today and compare the prediction to what is actually going on. If the two do not match, then a problem has been identified and a solution found. Addressing problems early is one of the cheapest ways to boost production.
The need for improved prediction
Wells that are big producers are typically watched by many eyes. Extensive instrumentation records pressures, temperatures, volumes and equipment performance parameters at regular intervals, which can be as often as every minute. This enables a large staff to quickly respond to unusual readings.
However, engineers and instruments are expensive and, in oilfields with multiple small wells, a single person is often responsible for the production of a hundred wells for which they receive information only once daily. Horror stories abound in which wells failed to produce at expected rates before an operator caught on and the situation was remedied.
Yet, if data can be used to effectively provide a metric against which to measure current production, a simple, inexpensive system that requires no instrumentation other than the production monitoring, well testing and production allocations can potentially save millions of dollars in deferred or lost production.
Having a grounded metric for production is an effective means of computing automatic warning alarms and of correlating production alarms to geographical location, geology and particular operators.
It’s not just engineers who need improved surveillance based on data that is already being collected and generally available. There are many other parties, like for instance non-operating partners, royalty owners and lenders, who also have an interest in making sure that production does not drop below reasonable levels. Yet, they have limited access to data other than production records.
The key to improving surveillance is improving prediction because the more accurately one can predict what production should be, the easier it is to spot deviation from the ideal.
How to achieve improved surveillance
The best tool to achieve improved surveillance is a library of accurate, probabilistic and unbiased oil and gas production forecasts for both individual and aggregate wells by reservoir, operator, basin or region. These forecasts should provide the full range of future production possibilities by combining map and production data with long-term production forecasts generated in an automated fashion and updated monthly.
But how can you forecast future volumes accurately to build your library? You will need a technology solution that gathers and interprets monthly oil, gas and water allocated volumes and records of working days (if available).
Such technology should consist of a physical part explaining data in terms that are physically plausible, and a statistical part explaining normal deviations from physical behaviour such as stimulations, shut-ins and bad data. Once the samples are drawn, they can be pushed forward to make a probabilistic forecast. Statistics are then computed over the samples. Rigorous calibration with blind testing are needed to ensure that the statistics of the samples match real data.
Because of its statistical nature, the solution can be used as an easily understood and controllable tool for surveillance. It can be used to set thresholds of a very specific nature – namely, the user can ask to be warned when production goes below a particular p-value and they can expect to receive warnings from each well under surveillance. The number of alarms can be reduced by using filters that reflect other factors such as changes in water cut, known shut-ins for maintenance and the like.
In summary, technology that helps you understand data and forecast events can provide constant watch over your operation and trigger alarms and alerts when defined events occur, allowing fewer staff to effectively manage larger portfolios and more assets.
As the volume of data from across organisations continues to grow, it becomes harder and harder to monitor KPIs and operating conditions through traditional dashboards relying on manual views and analysis. The sensible approach is automated exception management through user defined rules and conditions that monitor data and initiate alerts.
Conclusion
Knowing what to expect is ultimately the key to surveillance. Accurate forecasts facilitate the decision-making process for a variety of users, from financial investors looking to make sound investment to state officials wanting to make better resource-allocation decisions, from production engineers wanting to compare their wells’ performance with their competitors’ performance to upstream executives heavily involved in acquisitions and divestitures.
Having a better understanding of how wells are going to perform in the future is a powerful tool that represents a major change for the oil and gas data information sector. Better understanding always Leads
Leads
Leads or lead generation are an essential component of marketing and powerful tool by brokers. In its simplest form, leads can be defined as the outreach of customer interest or enquiry into products or services, most often associated with brokerages.These can be created for purposes such as list building, e-newsletter list acquisition, or for sales leads. Amongst marketers, such lists are one of their most important assets and instrumental to sales.There are a variety of methods for generating leads that traditionally fall under the mantle of advertising. However, this may also include non-paid sources such as organic search engine results or referrals from existing customersHow Are Leads Generated?In the FX space, nearly every brokerage has their own list of leads. How exactly these are generated varies to some extent. Most come from a composite of sources or activities.Specific parameters on the Internet such as personal referrals, telephone calls, or even conference attendance either by the company or telemarketers, through advertisements are the most common examples of this.Indeed, content marketing, search engine, and events are all effective ways in bolstering leads over time and account for the highest concentration of lead generation.Leads are also a powerful took by marketers to pursue new clients. This can involve customer relationship management (CRM) technology or follow ups in the form of contacting.The goal of these contacts is the conversion into a client. Simply obtaining a list of leads does not always correlate to business. This is where sales, follow ups, or other methods come into play.
Leads or lead generation are an essential component of marketing and powerful tool by brokers. In its simplest form, leads can be defined as the outreach of customer interest or enquiry into products or services, most often associated with brokerages.These can be created for purposes such as list building, e-newsletter list acquisition, or for sales leads. Amongst marketers, such lists are one of their most important assets and instrumental to sales.There are a variety of methods for generating leads that traditionally fall under the mantle of advertising. However, this may also include non-paid sources such as organic search engine results or referrals from existing customersHow Are Leads Generated?In the FX space, nearly every brokerage has their own list of leads. How exactly these are generated varies to some extent. Most come from a composite of sources or activities.Specific parameters on the Internet such as personal referrals, telephone calls, or even conference attendance either by the company or telemarketers, through advertisements are the most common examples of this.Indeed, content marketing, search engine, and events are all effective ways in bolstering leads over time and account for the highest concentration of lead generation.Leads are also a powerful took by marketers to pursue new clients. This can involve customer relationship management (CRM) technology or follow ups in the form of contacting.The goal of these contacts is the conversion into a client. Simply obtaining a list of leads does not always correlate to business. This is where sales, follow ups, or other methods come into play.
Read this Term to better decisions.
This article was written by Grant Eggleton, Vice President, Global Production Solutions at P2 Energy Solutions.