利用開放性熱裂解實驗資料建構較實用之石油生成動力學參數

Abstract

石油動力學的研究搭配生油岩的熱演化史曲線可以幫助我們得知生油的時間、溫度、深度甚至油產量。模擬生油岩生油的動力學程式往往給相同類型的生油岩相同的動力學參數，但近來更多的研究顯示，即使相同類型的生油岩其動力學的參數也會有個別的差異。因此，給予不同生油岩不同的動力學參數便成為一個必要的課題。由於單一生油岩常利用開放性熱裂解實驗以得知其生油潛力，因此，利用既有的開放性熱裂解實驗所得到的各項參數資料來預估單一生油岩的動力學參數即為本篇研究的第一個主要目的。但根據Lewan博士的研究，利用水合熱裂解實驗所得到的動力學參數來做模擬，較能趨近自然界盆地的生油過程。但進行水合熱裂解實驗的步驟流程十分繁雜。相較於開放性熱裂解實驗，它需要較多的生油岩樣本和實驗時間。因此，本研究第二個研究目的即是希望能從既有普遍的開放性熱裂解實驗參數來取得較準確的水合熱裂解動力學參數。此外，因自水合熱裂解實驗所得到的動力學參數僅是單一的活化能和單一的頻率因子，但自然界的油母質是由許多有機物質所組成的，其生油的過程中必然包含了相當多不同的反應。不論此反應是以平行的方式或序列的方式進行，我們都可以合理的假設這諸多的反應有著不同的活化能，而反應之多使得活化能可以呈現連續性或離散性的分布。然而，不同的活化能所搭配的頻率因子也理所當然的不同。因此，本篇亦嘗試修正自水合熱裂解所得的單一活化能和單一頻率因子成為離散性的分布。 藉由對比開放性熱裂解實驗的各項參數和其權重活化能（各單一活化能乘以其所占所有活化能的比例之總合），第一研究目的即可達成。但由於結果不如預期，本篇研究便製造新的參數以利與權重活化能有較佳的對比結果。利用對比「生油岩活化能分布的形態曲線」與「利用程式模擬的生油速度曲線」之「半高寬」和「選定的活化能與最高生油速度的溫度」來將此兩種曲線量化。量化的結果顯示生油岩活化能分布的形態曲線與利用程式模擬的生油速度曲線存在著高相似性。如此只要能再找出更佳的參數對比，便能提供一種簡易的方法：不需程式計算活化能曲線也能從生油岩實驗的加熱曲線得知其活化能的分布。 本篇研究發現開放性熱裂解實驗參數Tmax和水合熱裂解的單一活化能呈現良好的正相關關係。如此，我們只需經由簡易的計算，便能從既有的開放性熱裂解實驗參數Tmax得知能夠良好預測盆地生油過程的水合熱裂解動力學參數，如此本篇第二個研究目的也順利達成。 本篇研究採取開放性熱裂解實驗所得的活化能離散性分布曲線，保留其活化能間的比例並將整個分布曲線平移至其權重（各單一活化能乘以其所占所有活化能的比例之總合）所計算出的單一活化能與水合熱裂解的單一活化能相等，來修正自水合熱裂解所得的單一活化能成為一個離散性的分布曲線。再搭配活化能和頻率因子間良好的線性正相關，我們便又可以得到頻率因子的離散性分布曲線。 在程式中給定自然界的升溫速率，利用預測出單一的活化能和頻率因子做生油實驗的模擬，我們發現，生油岩只要維持在其開放性熱裂解實驗參數Tmax和水合熱裂解的單一活化能之線性相關中誤差小於百分之十，利用預測的水合熱裂解動力學參數將能比利用開放性熱裂解實驗所得的動力學參數還要更能貼近自然界中生油岩的生油過程。此外，利用修正後所得的離散性分布活化能和頻率因子來模擬生油過程，其結果將會和利用水合熱裂解所得的單一活化能和頻率因子所模擬的結果十分相近。如此，雖然無法證實利用活化能和頻率因子的分布曲線是否比利用單一活化能和頻率因子會得到更佳貼近自然界的生油過程，不過兩者間相近的模擬結果也間接證實了利用活化能分布和頻率因子分布來預測自然界中盆地產油過程是合理且可行的。 本實驗提供不必做繁雜的水合熱裂解實驗，只要經由簡易的計算方法，便能將既有的開放性熱裂解實驗參數經由計算，轉化成能模擬出較貼近自然界實際生油過程的水合熱裂解之動力學參數。並且藉由發現活化能分布曲線和程式模擬的生油速度曲線的相似，重新認識活化能在生油過程中所扮演的角色，並提供藉由開放性熱裂解實驗的加熱曲線轉換成活化能分布曲線的可行性。

Kinetic information is essential to predict the temperature, timing or depth of hydrocarbon generation within a hydrocarbon system. The most common practice in basin modeling uses default kinetic parameters (activation energy, Ea and frequency factor, A) based on three major kerogen types. Recent study revealed that the use of the default parameters of kerogen type can introduce unacceptable errors into modeling, suggesting the necessity to consider the characteristics of individual target source, although the measurement of kinetic parameters is by no means routine. Therefore, this study aims at prediction of kinetic information for individual source rock using mainly its Rock-Eval parameters. The present modeling revealed a systematic link between the shape of the discrete distribution of activation energies and the shapes of calculated reaction rates in both geological and laboratorial heating rates. New parameters were derived to quantify the similarities. Good correlations between lab-S2 and geo-S2 half high width with Ea half high width, strongly implying the resemblance in their shapes. Moreover, good correlation between the “selected Ea” and geo-Tpeak also suggests a delicate resemblance in the configuration of their shapes. Similarities in the shape of geo-S2 peak and lab-S2 peak with the shape of activation energy distribution may imply that it’s possible for deriving kinetic information with only Rock-Eval data, though more correlations may need for exact derivation; however, the role of activation energy in the reflection of reaction rates was utterly revealed. Recently it has been shown that hydrous pyrolysis conditions could simulate the natural conditions better and its applications were supported by two case studies. The kinetic information from hydrous pyrolysis experiment was then required for accurately predicting hydrocarbon generation. However, the closed system hydrous pyrolysis experiment is much more tedious and needs large amount of samples relative to open-system pyrolysis. Therefore, the second aim of our study is the derivation of convincing distributed activation energies of hydrous pyrolysis from only routine open-system Rock-Eval data. Our results unveiled that there was a good correlation between open-system Rock-Eval parameter Tmax and the activation energy (Ea) derived from hydrous pyrolysis. The single Ea of hydrous pyrolysis could be predicted from Tmax based on the correlation, while the frequency factor (A) was estimated based on the linear relationship between single Ea and log A. Because the Ea distribution is more rational than single Ea, we refined the predicted single hydrous pyrolysis Ea into a discrete distribution of Ea by shifting the pattern of Ea distribution from open-system pyrolysis until the weight mean Ea distribution equalled to the single hydrous pyrolysis Ea. Therefore, our predicted kinetic information can have the benefits of both experiments – the convincing Ea and A related to the hydrous pyrolysis and also the rational distribution of Ea from open-system Rock-Eval pyrolysis. Though such refined model with a discrete distribution of activation energies and frequency factors still need further verifications in the field, it showed in the transformation curves at geological heating rate were similar to that of original hydrous model still imply the adaptation for usage. The study offers a new approach as a simple method for obtaining improved kinetic parameters of hydrous pyrolysis (better than using the kinetic parameters of open-system pyrolysis) with only routine open-system Rock-Eval data, which will allow us for better estimating hydrocarbon generation.