Linear Multivariable Control of Underbalanced-Drilling Operations

Abstract

Summary This work presents a new multivariable controller for management of topside and bottomhole objectives during underbalanced drilling (UBD). A model predictive control (MPC) solution is used to control pressures, rate of penetration (ROP), and flow downhole while also ensuring that the topside processing constraints are respected. With automated control, it is possible to reduce nonproductive time (NPT), improve safety, and operate closer to the process constraints. MPC is a good fit for UBD because of its easy inherent handling of multiple objectives and constraints. With good pressure control, it is in some cases also possible to reduce the number of casing strings. The control solution is evaluated through simulations in a high-fidelity multiphase-flow oil and gas simulator (OLGA). It is shown that we can meet multiple objectives both at the surface and at different locations in the well. The optimization problem is solved with good results well within the given time constraints. The used linear prediction models are relatively easy to understand and maintain. They are also fast and well-suited for optimization and predictions. However, the process is nonlinear, and the linear models will be less accurate as the process conditions change. Retuning or model adaptation might be required to obtain the desired performance. It is possible to include nonlinear models in the control framework, referred to as nonlinear MPC (NMPC), but this will add complexity and require more computational power.

Application of Implicit Pressure-Explicit Saturation Method to Predict Filtrated Mud Saturation Impact on the Hydrocarbon Reservoirs Formation Damage

“…Underbalanced drilling operations (henceforth; UBD) is considered as one of the drilling procedures in the drilling of hydrocarbon wells. In this process, the wellbore pressure is fewer than the formation of static pressure [1][2][3][4][5]. By drilling the well, fluid flow through the porous media has been increased, and the hydrocarbon could flow to the surface via wellbore annulus.…”

Model predictive control and estimation of managed pressure drilling using a real-time high fidelity flow model

“…MPC has found favor in a variety of industries for several reasons, including its capability of handling multiple inputs and outputs, and its ability to handle input and state constraints [5,6,7]. In MPD applications, researchers have exploited these capabilities and have developed MPC systems that control pressure, flow, and rate of penetration (ROP) simultaneously [8,9], control pressure at two different locations in the well [10], and handle substantial heave motion on drill rigs subject to ocean surface disturbances [11,12].…”

“…Because such models necessarily omit certain physical effects, the compromised accuracy of these reduced order models is inevitable; studies suggest improved results could be achieved with more accurate models [8,12]. Model errors due to structural model mismatch become more prominent during transient periods caused by operational changes.…”

Internet of underground things‐assisted performance evaluation for underbalanced drilling telemetry systems

“…4,5 There are a number of natural resources accessible to agricultural lands and surface ecosystems. 6 The Internet of Underground Things (IoUT) is a technology that offers smart petroleum and gas fields, intelligent agriculture fields and intelligent seismic quality checks to effectively use all of these tools. 7 It is difficult to use the IoUT system, with small underground sensors, long-term communication technologies, effective networking approaches and precise localization technologies.…”

“…There are a number of natural resources accessible to agricultural lands and surface ecosystems 6 . The Internet of Underground Things (IoUT) is a technology that offers smart petroleum and gas fields, intelligent agriculture fields and intelligent seismic quality checks to effectively use all of these tools 7 .…”