Halliburton Energy Services, Inc.

University of Texas System

Halliburton Services

Robert Phillip Darbe

Nazli Demirer

Umut Zalluhoglu

Vy Pho

Shahin Tasoujian

Ketan Chimanlal Bhaidasna

David Leon Meadows

Julien Marck

Siyang Song

Yuanyan Chen

Khunsa Hisham

Jeffery Dwane Karcher

Benjamin John Iverson

Walmy Cuello Jimenez

Jian Wu

Dongmei Chen

Eirik Hansen

Robert Lee Browning

Alexander Mathew Keller

Christopher Lynn Gordon

Vytautas Usaitis

Keith Douglas Pewitt

Julien Christian Valery Marek

Ronnie Glen Morgan

Henry Eugene Rogers

Lonnie Carl Helms

Paul Joseph Jones

David L Meadows

Zhijie Sun

Yiming Zhao

Sudhir Kumar Gupta

David Bennett

Qiuying Gu

Yang Liu

Yang Yu

Chase Russell Gamwell

Marcus Allen Duffy

Atchyuta Ramayya Venna

He Zhang

Kaixiao Tian

Jiamin Xu

Julien Christian Valery Marck

Rick Bradshaw

Tingting Zeng

Tianheng Feng

Misty Dawn Rowe

Peter John Hornblower

A method for calibrating a steering model may comprise estimating an initial condition for one or more variables in the steering model and calibrating the steering model with a Markov Chain Monte Carlo Simulation (MCMC). A drilling system may comprise a bottom hole assembly, a drill string connected to the bottom hole assembly, and an information handling system connected to the bottom hole assembly. The information handling system may be configured to process one or more measurements from the bottom hole assembly, calibrate a steering model based at least in part on the one or more measurements, adjust a control logic based at least in part on the steering model, and adjust the bottom hole assembly based at least in part on the control logic.

Probabilistic model calibration and estimation for drilling steering control

In directional drilling, a nonlinear Delay Differential Equation (DDE) model may be used for its high precision in predicting how a borehole may be drilled according to a well plan. To address challenges associated with real-time control of a drill drilling wellbore, techniques of generalized feedback linearization, finite element concept, and zero-order hold discretization may be used to transform a nonlinear DDE model into discretized domain with a linear Ordinary Differential Equation (ODE) form. Following this transformation, a novel optimization framework may be used to concurrently determine optimal control inputs and solve a linear complementarity problem (LCP). The validity of both the discretized model and the optimization strategy may be verified by comparing modeled results with real-world results. Subsequent closed-loop simulations demonstrate the ability of the proposed model predictive control to maintain alignment of a drill string with a planned well trajectory, even in the presence of disturbances and noise.

Nonlinear model predictive control for directional drilling applications

One or more sensors are positioned downhole in a wellbore of a geological formation. One or more survey directional measurements associated with the wellbore are performed and one or more continuous directional measurements associated with the wellbore are performed. A wellbore trajectory model projects a trajectory of the wellbore to respective measured depths at which at least one of the one or more survey directional measurements and one or more continuous directional measurements were performed or obtained to predict one or more directional measurements of the wellbore at those depths. One or more differences are determined between the predicted one or more directional measurements and at least one of the one or more survey directional measurements and the one or more continuous directional measurements. The wellbore trajectory model is calibrated based on the one or more differences between predicted measurements and performed measurements.

Wellbore trajectory model calibration for directional drilling

An intuitive automated geosteering interpretation method and system that creates a geological model of a subterranean formation using an adaptive combined heatmap is provided. The adaptive combined heatmap is generated from individual heatmaps that each correspond to a specific type of drilling data (e.g. sensor readings) and then combined with adaptive weightings. An example of an automated method of drilling a target well using a well plan, includes: (1) generating at least two individual heatmaps for a subterranean formation using target well drilling data from a target well and offset well data from at least one offset well, wherein each of the individual heatmaps correspond to a specific type of drilling data, (2) generating an adaptive combined heatmap from the individual heatmaps, (3) determining a path of the target well in the subterranean formation based on the adaptive combined heatmap, and (4) updating the well plan according to the path.

Real-time automated geosteering interpretation using adaptive combined heatmaps

Aspects of the subject technology relate to systems and methods of controlling a drill string having a steerable bit when drilling a wellbore through a substrate. A deterministic model of a directional behavior of the drill string is developed that includes a drill string state, one or more drill parameters associated with the drill string, and one or more substrate parameters associated with the substrate. A stochastic differential model of the directional behavior of the drill string is then developed by replacing the state and each of the parameters of the deterministic model with respective probability distributions and adding feedback. The stochastic differential model is reduced to a truncated stochastic model by substituting a predetermined number of terms of a generalized polynomial chaos expansion for each probability distribution and then evaluating the expectations.

Optimal probabilistic steering control of directional drilling systems

A computer-implemented method for drilling a curve section of a wellbore in a subsurface formation. The method comprises obtaining a curvature setpoint. The method comprises obtaining drilling process feedback from a drilling assembly drilling the curve section of the wellbore. The method comprises updating a scheduling parameter vector based on the drilling process feedback. The method comprises updating a controller, wherein the controller is configured with the scheduling parameter vector. The method comprises determining an error based on the curvature setpoint and the drilling process feedback. The method comprises inputting the error into the controller. The method comprises determining, via the controller, steering decisions based on the error and the scheduling parameter vector.

Robust linear parameter varying control design for automated curvature cruise

Systems and methods for characterizing a subterranean formation that include a drill bit for drilling a deviated wellbore through the formation and that repeatedly changes direction. Also included is a directional drilling system and measurement equipment to detect a property of the formation. A trajectory control system is operable to: receive a well log; determine a property of the wellbore; segment the well log based on the property; and correlate one of the segments with the well log previous to the segment to determine a matching score for the segment. If the matching score is equal to or greater than a minimum threshold, the segment was taken in the formation and at the same stratigraphical true vertical depth ('STVD'). If the matching score is less than the minimum threshold, the segment was not taken in the formation and not at the same STVD and the STVD is estimated.

Systems and methods for characterizing a subterranean formation having a formation boundary surface

A geosteering subsystem has been designed to detect patterns in measurements that correlate to geological events and use statical analysis to interpret the geological events corresponding to control points and update a geological model based on the statistical analysis-based interpretations. The subsystem evaluates obtained measurements against predetermined patterns that correlate to geological events. If a pattern is detected, the subsystem labels the corresponding wellbore position and/or measurements as a control point and passes on the measurements for interpretation. The subsystem generates interpretations of the control point based on the measurements and historic control points. For each interpretation, the subsystem generates a pseudo log based on offset logs. The subsystem then generates a posterior probability for each interpretation based on evaluating the corresponding pseudo log with respect to the measurements and formation knowledge. High posterior probability interpretations are selected for updating the geological model.

Geosteering interpretation using bayesian inference

A method may include dividing a wellplan into one or more section using a section detection algorithm, receiving a depth measurement of a drill bit or a bottom hole assembly located in a wellbore, utilizing the section detection algorithm and the depth measurement to identify a section of the wellplan from the one or more sections of the wellplan, and identifying a target based at least in part on the identified section. The method may further include determining one or more steering commands based at least in part on the target and a control algorithm and steering the bottom hole assembly to the target using the one or more steering commands.

Automated vertical-curve-lateral drilling

A system for regulating a pump down operation may include a controller, a motor drive, a parameter estimation unit and a controller design unit. In examples, the controller may be configured to identify a difference between a downhole tension set-point to an actual downhole tension. The motor drive may be configured to adjust a line speed set point of the motor drive based at least in part on the difference from the controller to create an actual line speed from the motor drive to follow the downhole tension set-point. The parameter estimation unit may be configured to produce a fluid drag coefficient, a friction coefficient, and a line speed delay constant. The controller design unit may be configured to send one or more control gains to the controller based at least in part on the fluid drag coefficient, the friction coefficient, and the line speed delay constant.

Model based adaptive control scheme for pump down operation

A method comprises generating, via a pulse generator, a first pressure pulse in a fluid in a pipe and detecting, via a transducer, a first reflected pressure pulse based on the first pressure pulse, wherein the first pressure pulse is reflected by a pipe feature to generate the first reflected pressure pulse. The method comprises generating, with the pulse generator, a second pressure pulse at a later time relative to generating the first pressure pulse, wherein timing of generating of the second pressure pulse is such that the second pressure pulse is superposed with the first reflected pressure pulse such that an amplitude of the second pressure pulse is greater than an amplitude of the first pressure pulse. The method comprises detecting, via the transducer, a second reflected pressure pulse based on the second pressure pulse, and detecting a pipe feature based on the second reflected pressure pulse.

Detection of pipe features with pressure pulses

Aspects of the subject technology relate to systems and methods for improving mud motor trajectory controls. Systems and methods are provided for receiving control data from a mud motor trajectory controller, predefining a plurality of control modes based on the control data from the mud motor trajectory controller, achieving desired slide rotate ratios and toolface angles by solving an established objective function that mathematically represents operational preferences and system constraints for a selected control mode of the plurality of control modes, generating a modulation procedure that converts the slide-rotate ratios to a binary slide and rotate control sequence, and applying the modulation procedure to generate the binary slide and rotate control sequence.

Slide-rotate ratio mode optimization for mud motor trajectory control

Methods and compositions for estimating one or more downhole qualities of a drilling apparatus by obtaining axial acceleration data. The axial acceleration data may be acquired by measuring an axial acceleration of a bottom hole assembly. The axial acceleration may then be used to estimate one or more of a downward weight on a drill bit of the drilling apparatus or a rate of penetration of the drill bit.

Methods for estimating downhole weight on bit and rate of penetration using acceleration measurements

A curvature cruise controller has been designed to maintain a desired curvature when drilling a curve section of a wellbore. The curvature cruise controller utilizes a model-based controller (such as an LQR controller) to generate steering inputs based on the desired curvature and attitude setpoints and real time measurements. The model-based controller is configured with a wellbore propagation model that characterizes the tool's response dynamics. The curvature cruise controller utilizes real time drilling operation parameters measurements to tune the model-based controller.

Model-based curvature cruise control design

A well path for a directional drilling device may be updated by determining a perturbation for a new well path from a current bottom-hole assembly position to a target using the initial well plan (or any well path of reference) as a reference. An initial well plan may be determined for a directional drilling device along with a current actual borehole position of a drilling device along with other parameters such as attitude. With this, a perturbation to the well plan based on the current actual borehole position of a drilling device and a subterranean target maybe determined. Based on this perturbation an updated borehole path may be determined.

Perturbation based well path reconstruction

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Duncan, OK, United States of America

Robert Phillip Darbe