Qualitative Early Screening Tools for CCUS Location and Its Application for Tangguh Vorwata EGR CCUS

The oil and gas industry has recently seen an increase in Carbon Capture Utilization and Storage (CCUS) activity. CCUS and hydrocarbon exploration & development share a lot of things in common, primarily in the interpretation and assessment of subsurface. While numerous publications are available on exploring hydrocarbon, fewer published references exist for researching and screening high-potential CCUS locations. This paper is intended to share the qualitative screening tools to look for high potential CCUS location and its application in Tangguh CCUS, Vorwata Enhanced Gas Recovery (EGR) program. Early screening for CCUS focuses on identifying all relevant aspects, which are the source, the transport, and the storage of the CO2. Each element has three different classifications, as favorable, neutral, and unfavorable, based on defined metrics. For example, the metric for source is the distance to the storage; is it near or far? Near CO2 source to storage site will be classified as favorable, and far distance will be classified as unfavorable. A neutral classification is applied to the aspect that needs further data or analysis. The metric definition is adjusted as required. For example, if the preferred location is offshore, then the metric can be set to favor the offshore location and vice versa. This screening has been applied to Tangguh CCUS, Vorwata Field. Vorwata field is an offshore field located in Bintuni Bay, Papua Barat province, Indonesia, which was discovered in the 1990s. The result from the screening tools application shows that Vorwata, in all aspects of source, transport, and storage of CO2, is classified as favorable. The study suggests that this qualitative screening tool is useful in identifying a high potential location for the CCUS program. It offers a systematic approach to identifying a high potential area of focus. At the same time, it also offers flexibility through different sets of agreed metrics for classification.