面向中国油田产量预测的天然气和液位数据集。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Yanlei Wang, Jian Lian, Chengjiang Li
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引用次数: 0

摘要

天然气是国民经济和公共福利的重要资源,在能源领域发挥着重要作用。地层水的存在往往会阻碍天然气的有效开采,这可能会对油井的产能和作业效率产生不利影响。因此,准确预测天然气产量和估算相关的流体聚集对于优化开采过程至关重要。为了提高天然气产量和相关流体聚集预测的准确性,本文以内蒙古地区山西组为研究对象,建立了多口井数据集。该数据集的时间跨度为2010年3月17日至2024年4月15日,包括井口压力、套管压力、每日甲醇注入量以及气、水和油的累计产量的详细记录。通过分析这些参数,我们可以识别出随时间变化的趋势和异常,这对于制定生产策略和减轻流体积聚对气井的影响至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A dataset of Natural Gas and Liquid Level for Oil Field Production Prediction in China.

A dataset of Natural Gas and Liquid Level for Oil Field Production Prediction in China.

A dataset of Natural Gas and Liquid Level for Oil Field Production Prediction in China.

Natural gas, a critical resource for national economies and public welfare, plays a significant role in the energy sector. The efficient production of natural gas is often hindered by the presence of formation water, which can adversely affect well productivity and operational efficiency. Accurate prediction of natural gas production and estimation of associated fluid accumulation are therefore paramount for optimizing extraction processes. This study introduces a dataset compiled from multiple wells in the Inner Mongolia region, primarily targeting the Shanxi Formation, with the aim of enhancing the predictive accuracy of natural gas production and associated fluid accumulation. The dataset, spanning from March 17, 2010, to April 15, 2024, includes detailed records of wellhead pressure, casing pressure, daily methanol injection, and cumulative production volumes of gas, water, and oil. By analyzing these parameters, we can identify trends and anomalies over time, which are essential for refining production strategies and mitigating the impact of fluid accumulation on gas wells.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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