Cross-domain data analysis is arguably the most important part of oilfield data analytics. While it enables holistic process optimization, it is also challenging to execute. Data are often scattered across different databases making it complex to join and may require multidomain expertise to properly analyze. Here, processing and analysis of data collected by three well construction business lines of the same service company were performed to establish a link between drilling fluid properties and drilling performance.
The data engineering workflow starts by taking information from a single service company and combining that information about drilling operations, drill bits, and drilling fluids into a single dataset. Metadata including locations, operators, and wells are then mapped, and overlapping attributes are unified and reconciled. Data is further processed to extract relevant drilling performance metrics and drilling fluid properties and then labeled by well, section, and drilling run. The resultant data workflow enables detailed analysis, focusing on particular locations, drilling practices, hole conditions, and fluids.
The joined, cleaned, and processed dataset includes information from thousands of wells drilled globally since 2016. The datasets from different sources differ in the level of detail, but are complementary to each other, providing a broader picture when merged. The data is organized and visualized on dashboards, enabling in-depth analysis through intuitive filtering on a variety of conditions. These conditions may include location, drilling run type, depth, used drill bits and tools, and drilling fluid type and properties. The main drilling performance metrics are distance drilled per run and run duration. These are used to calculate the run average rate of penetration (ROP). Reasons for pulling out of the hole (POOH) and risks for POOH are extracted from text comments of the daily drilling reports. This enables the tracking of abnormal run terminations due to drilling tool failures. It also enables tracking of wellbore integrity, and substandard drilling and hole conditioning practices, especially at section total depth (TD) or because of drilling fluid issues. Aggregated metrics of minimum, maximum, and median are used for high-level data evaluation. Statistical significance of effects and causality are analyzed in detail on selected cases. Based on the data, several examples of such analyses are created that focus on the effects of water-based fluid vs. oil-based fluid, on drilling performance in the major oil fields in the United States.
Holistic analysis of the effects of drilling fluids on drilling performance becomes possible through the well construction cross-domain data fusion. The developed workflow enables analysis of drilling fluid-related big data, covering tens of thousands of wells globally. The analysis results are expected to improve drilling efficiency and reliability and ultimately reduce operators' total well expenditures.
