Currently in the petroleum industry, operators often flare the produced gas instead of commodifying it. The flaring magnitudes are large in some states, which constitute problems with energy waste and CO2 emissions. In North Dakota, operators are required to estimate and report the volume flared. The questions are, how good is the quality of this reporting, and what insights can be drawn from it? Apart from the company-reported statistics, which are available from the North Dakota Industrial Commission (NDIC), flared volumes can be estimated via satellite remote sensing, serving as an unbiased benchmark. Since interpretation of the Landsat 8 imagery is hindered by artifacts due to glow, the estimated volumes based on the Visible Infrared Imaging Radiometer Suite (VIIRS) are used. Reverse geocoding is performed for comparing and contrasting the NDIC and VIIRS data at different levels, such as county and oilfield. With all the data gathered and preprocessed, Bayesian learning implemented by MCMC methods is performed to address three problems: county level model development, flaring time series analytics, and distribution estimation. First, there is heterogeneity among the different counties, in the associations between the NDIC and VIIRS volumes. In light of such, models are developed for each county by exploiting hierarchical models. Second, the flaring time series, albeit noisy, contains information regarding trends and patterns, which provide some insights into operator approaches. Gaussian processes are found to be effective in many different pattern recognition scenarios. Third, distributional insights are obtained through unsupervised learning. The negative binomial and GMMs are found to effectively describe the oilfield flare count and flared volume distributions, respectively. Finally, a nearest-neighbor-based approach for operator level monitoring and analytics is introduced.
翻译:目前石油工业中,运营商经常点燃天然气生产量而不是商品化。燃烧量在某些州很大,对能源浪费和二氧化碳排放构成问题。在北达科他州,运营商需要估算和报告燃烧量。问题在于,这一报告的质量有多好,可以从中得出什么见解?除了从北达科塔工业委员会(NDIC)得到的公司报告统计数据之外,通过卫星遥感来估计燃烧量,作为公正的基准。由于Landasat 8图像的判读受到艺术品的阻碍,因此使用基于可见的红外成像辐射计(VIIRS)的估算型号。为了比较和对比NDIC和VIIRS在不同级别的数据,例如郡和油田的数据质量,重新进行地理分解。由于所有数据收集和预处理的数据,Bayesian的学习用MMC方法进行公正的评估,从而解决了三个问题: 县级模型的开发、Flaring时间序列的解析,以及基于可见红外成型号的分布模型(VII)的估算型号。最后,在州一级,对数字流流流流和分布的模型中,通过直径序中,对数字的计算结果的计算为直径。