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云 Hortonworks 是领导者。阅读 Forrester Wave。



Assets everywhere. People everywhere. Logistics everywhere. The petroleum industry has a lot of moving parts, and pretty much every aspect of it is in constant flux. Like other industries, its infrastructure generates data of all kinds—sensor data from upstream, midstream, and downstream operations, geological and geophysical, drilling and completions data, geolocation, text files, video and more. Hortonworks enables provides IoT and predictive big data analytics for oil and gas, delivering the predictive analysis and data insights to optimize performance to keep this industry humming.


Universal changes in the availability of data are changing the petrochemicals business in ways similar to changes in telecom, retail and manufacturing. Advances in instrumentation, process automation, and collaboration multiply the available volume of new types of data like sensor, IoT, geolocation, weather and seismic data. These can be combined with “human-generated” data like market feeds, social media, email, text, and images for oil and gas big data analytics providing new insight.


通过测井分析(也称为 LAS 分析)加速创新

Large, complex datasets and rigid data models limit the pace of innovation for exploration and production, because they require petrophysicists and geoscientists to work with siloed, complex datasets that require a manual quality control (QC) process. LAS log analytics with HDP big data analytics for oil and gas allows scientists to ingest and query their disparate LAS data for use in predictive models. They can do this while leveraging existing statistical tools such as SAS or R to build new models and then rapidly iterate them with billions of measurements. Combining LAS data with production, lease, and treatment data can increase production and margins. Dynamic well logs normalize and merge 100s or 1000s of LAS files, providing a single view of well log curves, presented as new LAS files or images. With HDP, those consolidated logs also include much of the sensor data that used to be “out of normal range” because of anomalous readings from power spikes, calibration errors, and other exceptions. With HDP, an automated QC process can ingest all the data (good and bad) then scrub it to eliminate the anomalous readings and present a clear, single view of the data.


After identifying the ideal operating parameters (e.g. pump rates or fluid temperatures) that produce oil and gas at the highest margins, that information can go into a set point playbook. Maintaining the best set points for a well in real-time is a job for Apache Storm’s fault-tolerant, real-time oil and gas predictive analytics and alerts. Storm running in Hadoop can monitor variables like pump pressures, RPMs, flow rates, and temperatures, and then take corrective action if any of these set points deviate from pre-determined ranges. This data-rich framework helps the well operator save money and adjust operations as conditions change.


石油和天然气公司在联邦和私有土地上投标勘探和钻井权的多年租约。为租赁支付的价格是获得未来的不可预测烃类流而需要支付的目前已知成本。油井出租方可以降低与未来收益有关的不确定性并且更加精确地预测油井的产量,从而比竞争对手出价更高。Apache Hadoop 可以高效率存储图像文件、传感器数据和地震测量值,从而提供这种竞争优势。这为招标用的任何第三方调查手册增加了缺少的上下文。通过预测分析而获得这种独一无二信息的公司现在可以获得这一苦苦追寻的租约,或者能够找到“粗糙的钻石”并以折扣价租赁到这些油井。


Traditionally, operators gathered data on the status of pumps and wells through physical inspections (often in remote locations). This meant that inspection data was sparse and difficult to access, particularly considering the high value of the equipment in question and the potential health and safety impacts of accidents. Now, oil and gas IoT sensor data can stream into Hadoop from pumps, wells and other equipment much more frequently—and at lower cost—than collecting the same data manually. This helps guide skilled workers to do what sensors cannot: repair or replace machines. The machine data can be enriched with other data streams on weather, seismic activity or social media sentiment, to paint a more complete picture of what’s happening in the field. Algorithms then parse that large, multifaceted data set in Hadoop to discover subtle patterns and compare expected with actual outcomes. Did a piece of equipment fail sooner than expected, and if so, what similar gear might be at risk of doing the same? Data-driven, preventative upkeep keeps equipment running with less risk of accident and lower maintenance costs.


Oil companies need to manage the decline in production from their existing wells, since new discoveries are harder and harder to come by. Decline Curve Analysis (DCA) uses past production from a well to estimate future output. However, historic data usually shows constant production rates, whereas a well’s decline towards the end of its life follows a non-linear pattern—it usually declines more quickly as it depletes. When it comes to a well near the end of its life, past is not prologue. Production parameter optimization is intelligent management of the parameters that maximize a well’s useful life, such as pressures, flow rates, and thermal characteristics of injected fluid mixtures. Machine learning algorithms can analyze massive volumes of sensor data from multiple wells to determine the best combination of these controllable parameters. HDP’s powerful capabilities for data discovery and subsequent big data analytics for oil and gas analysis can help the well’s owner or lessee make the most of that resource.