Transocean is one of the largest offshore drilling companies that provides rig-based well construction services worldwide. It operates a fleet of versatile, mobile, offshore units comprised of midwater, deep water, ultra-deep water, and harsh environment floaters. The company’s dedication to innovation and constant performance improvement led them to search for the best technical solutions.
Offshore oil and gas well construction is a complex process that takes a considerable amount of time. It demands certain sequences of both manual and semi-automated operations carried out in unison, as safely and efficiently as possible. Different kinds of equipment are usually created and operated independently. However, at the rig, all technical units are integrated. Any kind of delay in machinery increases the critical time path, which reduces the efficiency of the overall performance, and can lead to financial losses.
The critical time path can vary depending on various factors, including:
- Equipment manufacturers and configurations
- Experience and fatigue of operations personnel
- Rig type and drill floor layout
- Weather conditions
- Equipment maintenance
- Wellbore conditions
It is hard to track the dependencies of such variations and resulting inefficiencies throughout the overall process. For this purpose, the engineers needed to focus first on the different states of equipment; decomposing the process to the simplest levels, then combining these states, and finally seeing how they work together in the scope of other factors, including high-safety standards.
To handle possible variations and inefficiencies of the well construction process, Transocean engineers needed to collect and assess measurements at dozens of rigs, including machine and crew timing. A tremendous amount of data had to be collected and then analyzed, and if they managed it manually, it would be too time-consuming. That is why engineers decided to build a well optimization simulation model based on the machine data, including control signals from the equipment. Simulation would help engineers build an oil well optimization digital twin, which would reflect and help analyze the interdependencies among various operations to reduce downtime. Simulation outputs would enable the managerial staff to determine the real reasons of time loss and find solutions.
When building the oil well optimization model, engineers focused specifically on the tripping stage of the well construction process. It usually takes 20-30% of the overall well construction time, and it is semi-automated, so there is tremendous variability in the equipment performance. The process consists of operations such as block retracting, block hoisting, and bringing the pipe to the well center, some of which are done simultaneously.
Using AnyLogic oil well construction process simulation software, and leveraging state machine algorithms and discrete event process modeling, Transocean was able to analyze the tripping operations in real time. The whole process was decomposed into a hierarchal system of four main operations that were also split into functional elements down to the simplest levels. This allowed the engineers to create the state machine model. This well construction optimization model was integrated with a discrete event process model, which altogether represented the system of the entire tripping process with the built-in machine logic of 4-5 devices.
The AnyLogic simulation allowed engineers to put in machine data to both models in real time and receive a range of results that could be presented in dashboards and colored charts to identify machine states and process activities, capture descriptive statistics, and analyze the time critical to operations for its minimization.
The application output the data to an SQL database, and then to BI-tools. This permitted the management and operations teams to have the necessary visual aids, and with the given information, look for the exact reasons of downtime and performance inefficiencies.
With AnyLogic oil well construction process simulation, Transocean engineers were able to represent the whole tripping stage of the well construction process. With real-time machine data and detailed representation of operations, the oil well optimization model performed as a digital twin, aiding in analyzing well construction activities. Model statistics were fed back to operations personnel and rig managers, allowing them to assess how well crews were performing and identify causes of time loss. The initial results indicated that over 20% of time could be saved by implementing the digital twin.
Future implementation of the oil well construction process simulation for this project might result in assessing a new well profile and predicting the oil well’s performance. The profile could be run through the model, essentially drilling a virtual well, and could provide future insights into performance of the well, considering material handling, resources needed, and logistics around the rig.