Data-processing and recognition of seepage and microseepage anomalies of acid-extractable hydrocarbons in the south slope of the Dongying depression, eastern China

• Relationships between near-surface seepages and oil/gas pools are often complex.
• This arises from surface interference, geological influences and anomaly diversity.
• Wavelet-analysis-based new methods for eliminating the interference and influences.
• Fundamental equations and methods for recognizing multi-type anomalies.
• Iterative use of these equations and methods provides more reliable anomalies.

The successful use of near-surface anomalous signals, exhibited in all petroliferous basins, must lead to great development in petroleum exploration. The elementary equations and methods for objective and accurate separation of anomalous signals from background signals are the most critical requirements in these surface geochemical surveys. Therefore, we have worked up a series of fundamental equations and methods for eliminating surface interference and influences of geological conditions on anomaly intensities and recognizing multi-type anomalies of uni- and multi-variates, by using statistics, fractal geometry, wavelet analysis and artificial neural networks. These equations illustrate that the threshold (i.e. the boundary between background and micro-seepage anomalies) is determined by means, standard deviations and prior probabilities of background and micro-seepage anomalies when multi-normality is met, and that the traditional equation for the threshold (background mean plus one or two standard deviations) is not correct. In the face of many new equations and methods, it is important how to comprehensively use them to resolve the problems in geochemical surveys. A geochemical survey in the south slope of the Dongying depression, eastern China provides a good opportunity to address this issue. The anomalies obtained with the traditional equation do not reflect oil/gas fields. This study reveals three problems in the previous data-processing and anomaly recognition: (1) the surface interference caused by the variation of soil composition was not identified and removed, (2) the influence of caprock thickness on anomaly intensities was not revealed and eliminated, and (3) the micro-seepage (related to oil/gas pools) and seepage (related to faults) anomalies were not separated and correctly recognized. Furthermore, the interaction of these problems makes the data-processing and anomaly recognition more difficult. To ensure effective elimination of the surface interference and caprock thickness influence and correct recognition of uni-variate anomalies, we iteratively applied the wavelet-analysis-based methods to eliminate the surface interference and caprock thickness influence, and statistical methods with new fundamental equations to recognize uni-variate anomalies of seepage and microseepage. And then, multi-variate anomalies of seepage and microseepage were recognized with our fundamental equations and the corresponding methods. In the results, the seepage anomalies display a string-bead-shaped pattern and are distributed along faults, and the micro-seepage anomalies are ring-shaped and coincide with oil/gas pools, sandbodies or traps. Therefore, reprocessing of geochemical data with our fundamental equations and methods combined in an iterative manner can resolve the complicated problems in data-processing and anomaly recognition and thus greatly improve the predictive capability of surface geochemical survey.

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