Abstract: The classification of big data algorithms in petroleum engineering is not well-defined, leading to suboptimal accuracy and low relevance when searching for big data algorithm applications across different fields of petroleum engineering. Based on the main on-site operating activities in the four fields of petroleum engineering covering exploration, development, production, and storage/transportation, the big data algorithms engaged in petroleum engineering are categorized into four groups: exploration algorithms, development algorithms, production algorithms, and storage/transportation algorithms. The concepts of these four types of algorithms are clarified. Totally 53 papers, highly relevant with oil and gas big data algorithms, are picked from core journal databases over the past decade, from core journal databases over the pase decade. The algorithms involved in these papers are introduced into these four algorithm categories according to the applicable contents, creating the classification of big data algorithms in petroleum engineering by fields. In terms of algorithm in 53 papers, there are 7 subdivisions of exploration algorithms, 5 of development algorithms, 8 of production algorithms, and 7 of storage/transportation algorithms. After classification, the accuracy rate for selecting algorithms reached 100%, and the efficiency of algorithm selection also reached 100%, which is an improvement of 75 percentage points and 52 percentage points, respectively, compared to the pre-classification phase. Categorizing big data algorithms in petroleum engineering into four main groups based on application fields resolves the issues of low accuracy and weak relevance in selecting appropriate algorithms for research and applications big data algorithms. This method also provides a useful reference for classifying literature in other fields.