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WO2020028298A1 - Procédé de commande d'orientation géologique par apprentissage par renforcement - Google Patents

Procédé de commande d'orientation géologique par apprentissage par renforcement Download PDF

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Publication number
WO2020028298A1
WO2020028298A1 PCT/US2019/044036 US2019044036W WO2020028298A1 WO 2020028298 A1 WO2020028298 A1 WO 2020028298A1 US 2019044036 W US2019044036 W US 2019044036W WO 2020028298 A1 WO2020028298 A1 WO 2020028298A1
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WO
WIPO (PCT)
Prior art keywords
well
reward
combinations
geological
model
Prior art date
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Ceased
Application number
PCT/US2019/044036
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English (en)
Inventor
Neilkunal PANCHAL
Sami Mohammed Khair SULTAN
Minith Bharat JAIN
Patricia ASTRID
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shell Internationale Research Maatschappij BV
Shell USA Inc
Original Assignee
Shell Internationale Research Maatschappij BV
Shell Oil Co
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Filing date
Publication date
Application filed by Shell Internationale Research Maatschappij BV, Shell Oil Co filed Critical Shell Internationale Research Maatschappij BV
Priority to US17/263,986 priority Critical patent/US20210310347A1/en
Publication of WO2020028298A1 publication Critical patent/WO2020028298A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B7/00—Special methods or apparatus for drilling
    • E21B7/04—Directional drilling

Definitions

  • the present invention relates to the field of geosteering and, in particular, to a method for autonomous geosteering for a well-boring process.
  • rock destruction is guided by a drilling assembly.
  • the drilling assembly includes sensors and actuators for biasing the trajectory and determining the heading in addition to properties of the surrounding borehole media.
  • the intentional guiding of a trajectory to remain within the same rock or fluid and/or along a fluid boundary such as an oil/water contact or an oil/gas contact is known as geosteering.
  • Geosteering is drilling a horizontal wellbore that ideally is located within or near preferred rock layers. As interpretive analysis is performed while or after drilling, geosteering determines and communicates a wellbore's stratigraphic depth location in part by estimating local geometric bedding structure. Modern geosteering normally incorporates more dimensions of information, including insight from downhole data and quantitative correlation methods. Ultimately, geosteering provides explicit approximation of the location of nearby geologic beds in relationship to a wellbore and coordinate system.
  • Geosteering relies on mapping data acquired in the structural domain along the horizontal wellbore and into the stratigraphic depth domain.
  • Relative Stratigraphic Depth means that the depth in question is oriented in the stratigraphic depth direction and is relative to a geologic marker. Such a marker is typically chosen from type log data to be the top of the pay zone/target layer.
  • the actual drilling target or“sweet spot” is located at an onset stratigraphic distance from the top of the pay zone/target layer.
  • US8,892,407B2 (ExxonMobil) relates to a process for well trajectory planning.
  • the process involves receiving data relevant to drilling and completion of an oil or gas well, and to reservoir development.
  • Well trajectory and drilling and completion decision parameters are simultaneously calculated using a Markov decision process-based model that accounts for an uncertain parameter to optimize an objective function that generates a plan for drilling and completion of one or more oil or gas wells.
  • the objective function optimizes one or more performance metrics that include reservoir performance, well drilling performance, and financial performance, subject to satisfying constraints on the drilling.
  • a method for autonomous geosteering for a well-boring process comprising the steps of: (a) providing a trained function approximating agent; (b) determining a geological objective; (c) determining a sequence of control inputs to steer a well-boring tool towards the geological objective, wherein the trained function approximating agent is adapted to enact the sequence of control inputs upon receiving a signal from a measurement from the well-boring process.
  • Fig.1 illustrates a result of one embodiment of the present invention
  • Fig.2 illustrates one embodiment of a reward function suitable for the method of the present invention
  • Fig.3 is a graphical representation of the results of a first test of a simulation environment produced according to the method of the present invention
  • Fig.4 is a graphical representation of the results of a second test of a simulation environment produced according to the method of the present invention.
  • Fig.5 is a graphical representation of the results of a third test of a simulation environment produced according to the method of the present invention.
  • Fig.6 is a graphical representation of the results of a fourth test of a simulation environment produced according to the method of the present invention.
  • the present invention provides a method for autonomous geosteering using a trained function approximating agent.
  • the method is a computer-implemented method.
  • function approximating agent we mean a process for finding an underlying relationship from a given finite set of input-output data.
  • function approximating agents include neural networks, such as backpropagation-enabled processes, including deep learning, machine learning, frequency neural networks, Bayesian neural networks, Gaussian processes, polynomials, and derivative-free processes, such as annealing processes, evolutionary processes and sampling processes.
  • the function approximating agent is trained on a physical simulator approximating a real geological and drilling operation, for example, in the intended subterranean formation.
  • the function approximating agent may be trained by (a) providing an earth model defining boundaries between formation layers and petrophysical properties of the formation layers in a subterranean formation comprising data selected from the group consisting of seismic data, data from an offset well and combinations thereof, and producing a set of model coefficients; (b) providing a toolface input corresponding to the set of model coefficients to a drilling attitude model for determining a drilling attitude state; (c) determining a drill bit position in the subterranean formation from the drilling attitude state; (d) feeding the drill bit position to the training earth model, and determining an updated set of model coefficients for a predetermined interval and a set of signals representing physical properties of the subterranean formation for the drill bit position; (e) inputting the set of signals to a sensor model for producing at least one sensor output and determining a sensor reward from the at least one sensor output;(f) correlating the toolface input and the corresponding drilling attitude state, drill bit position, set of model
  • the drilling model for the simulation environment may be a kinematic model, a dynamical system model, a finite element model, a Markov decision process, and
  • Preferred examples of function approximating agents include stochastic clustering and pattern matching, greedy Monte Carlo, differential dynamic programming, and combinations and derivatives thereof.
  • the function approximating agent is trained by reinforcement learning, deep reinforcement learning, approximate dynamic programming, stochastic optimal control, and combinations thereof.
  • a sequence of control inputs is determined to steer a well-boring tool towards a geological objective.
  • the geological objective may, for example, without limitation, a relative 1D position, a relative 2D position, a relative 3D position, a dip angle, a strike angle, and combinations thereof.
  • the sequence of control inputs includes, without limitation, curvature, roll angle, set points for inclination, set points for azimuth, Euler angle, rotation matrix quaternions, angle axis, position vector, position Cartesian, polar, and combinations thereof
  • the trained function approximating agent is adapted to enact the sequence of control inputs upon receiving a signal from a measurement from the well-boring process.
  • a reward function is used in the method of the present invention. More preferably, the reward function is based on a reward objective including, without limitation, shortest distance to the geological objective, lowest percentage of out-of-zone time, lowest deviation from targeted relative stratigraphic depth, lowest deviation from a well plan, reaching a target waypoint, consistency with target heading, lowest number of steering correction control signals, minimizing angular deviation, and combinations thereof. More preferably, the reward function further includes, without limitation, negative rewards for reduced drilling speed, increased wear on drill bit, proximity to region identified as being nearby a well, proximity to region having a geological feature that should be avoided, and combinations thereof. Preferably, the reward function includes negative rewards for angular deviation, tortuosity, excess curvature, and combinations thereof.
  • Examples of a geological objective include an existing well, a target well path for a future well, simulations of an existing well, simulations of a target well path for a future well, and combinations thereof.
  • a target well path avoids collision with an existing well.
  • the reward function has a positive reward for colliding with the geological objective.
  • the reward function includes a positive episodic reward for an episodic action including, without limitation, reaching a predetermined end depth, reaching a target zone, extending a predetermined number of feet in a target zone, and combinations thereof.
  • the reward function may also include a negative reward for an episodic action including, without limitation, missing the target, deviating too far from a predetermined geological datum, entering into a no-go zone, and combinations thereof.
  • Examples of a no-go zone include, without limitation, lease lines, permeability, porosity, petrophysical properties, nearby wells, and the like.
  • Examples of a geological datum can be, for example, without limitation, a rock formation boundary, a geological feature, an offset well, an oil/water contact, an oil/gas contact, an oil/tar contact and combinations thereof.
  • the well-boring process is modeled as a Markov decision process.
  • Model Predictive Control which reframes the task of following a trajectory as an optimization problem.
  • the solution to the optimization problem is the optimal trajectory.
  • Model Predictive Control involves simulating different actuator inputs, predicting the resulting trajectory and selecting that trajectory with a minimum cost. Parameters involved are starting state, process model, reference trajectory, errors, length, duration, cost function and constraints.
  • Fig.1 illustrates one embodiment of a reward function.
  • the vertical dashed lines represent a user-defined tolerance.
  • the shape of the curve can also be selected by the user, depending on the user’s objective.
  • the reward function is selected to balance precision and speed, in this case with a coasting threshold of 0.60 m (2 ft) and a coasting bonus of 0.3.
  • the coasting threshold is the distance from the well plan at which the user wants the bottom hole assembly to prioritize speed over accuracy.
  • a synthetic well was generated based on an actual gamma ray log.
  • the real data is identified by a type log gamma ray plot 62.
  • a boundary 64 representing the top of a target formation was determined and a synthetic true well path 66 was generated.
  • Region 72 represents a 1.5-m (5- foot) error about the true well path 66
  • region 74 represents a 3-m (10-foot) error about the well path 66.
  • the goal of the test was to match the true well path 66 as best as possible.
  • Example 1– 4 the function approximating agent is described in co- pending application entitled“Process for Real Time Geological Localization with Bayesian Reinforcement Learning” filed in the USPTO on the same day as the present application, as provisional application US62/712,518 filed 31 July 2018, the entirety of which is incorporated by reference herein.
  • the Bayesian Reinforcement Learning (BRL) function approximating agent was trained according to the method described in co-pending application entitled “Method for Simulating a Coupled Geological and Drilling Environment” filed in the USPTO on the same day as the present application, as provisional application US62/712,490 filed 31 July 2018, the entirety of which is incorporated by reference herein.
  • Well log gamma ray data 76 was fed to the trained agent and a set of control inputs, in this case well inclination angle 78, was used to steer the well-boring along the true well path 66, according to the method described herein.

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  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Geology (AREA)
  • Mining & Mineral Resources (AREA)
  • Physics & Mathematics (AREA)
  • Environmental & Geological Engineering (AREA)
  • Fluid Mechanics (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geochemistry & Mineralogy (AREA)
  • Geophysics And Detection Of Objects (AREA)

Abstract

Un procédé de géo-orientation autonome pour un processus de forage utilise un agent d'approximation à fonction entraînée. Un objectif géologique est déterminé. Ensuite, à l'aide de l'agent d'approximation à fonction entraînée, une séquence d'entrées de commande est déterminée pour orienter un outil de forage vers l'objectif géologique. L'agent d'approximation à fonction entraînée est conçu pour agir sur la séquence d'entrées de commande lors de la réception d'un signal à partir d'une mesure émanant du processus de forage.
PCT/US2019/044036 2018-07-31 2019-07-30 Procédé de commande d'orientation géologique par apprentissage par renforcement Ceased WO2020028298A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US17/263,986 US20210310347A1 (en) 2018-07-31 2019-07-30 Method for geological steering control through reinforcement learning

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US201862712506P 2018-07-31 2018-07-31
US62/712,506 2018-07-31
EP18194442.2 2018-09-14
EP18194442 2018-09-14

Publications (1)

Publication Number Publication Date
WO2020028298A1 true WO2020028298A1 (fr) 2020-02-06

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113464120A (zh) * 2021-09-06 2021-10-01 中国石油集团川庆钻探工程有限公司 工具面状态的预测方法和系统、滑动定向钻井方法和系统
WO2022104324A1 (fr) * 2020-11-12 2022-05-19 Schlumberger Technology Corporation Système et procédé de décision de forage à agents multiples
US11828155B2 (en) 2019-05-21 2023-11-28 Schlumberger Technology Corporation Drilling control
US12534994B2 (en) 2023-09-29 2026-01-27 Schlumberger Technology Corporation Drilling control

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8892407B2 (en) 2008-10-01 2014-11-18 Exxonmobil Upstream Research Company Robust well trajectory planning
WO2018106748A1 (fr) * 2016-12-09 2018-06-14 Schlumberger Technology Corporation Heuristique de réseau neuronal d'opérations de champ

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8892407B2 (en) 2008-10-01 2014-11-18 Exxonmobil Upstream Research Company Robust well trajectory planning
WO2018106748A1 (fr) * 2016-12-09 2018-06-14 Schlumberger Technology Corporation Heuristique de réseau neuronal d'opérations de champ

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
JACOB POLLOCK ET AL: "Machine Learning for Improved Directional Drilling (OTC-28633-MS)", OFFSHORE TECHNOLOGY CONFERENCE 2018, 30 April 2018 (2018-04-30), Houston, Texas, USA, pages 1 - 9, XP055564922, ISBN: 978-1-61399-571-6, DOI: 10.4043/28633-MS *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11828155B2 (en) 2019-05-21 2023-11-28 Schlumberger Technology Corporation Drilling control
US12252975B2 (en) 2019-05-21 2025-03-18 Schlumberger Technology Corporation Drilling control
WO2022104324A1 (fr) * 2020-11-12 2022-05-19 Schlumberger Technology Corporation Système et procédé de décision de forage à agents multiples
US12071844B2 (en) 2020-11-12 2024-08-27 Schlumberger Technology Corporation Multi-agent drilling decision system and method
CN113464120A (zh) * 2021-09-06 2021-10-01 中国石油集团川庆钻探工程有限公司 工具面状态的预测方法和系统、滑动定向钻井方法和系统
CN113464120B (zh) * 2021-09-06 2021-12-03 中国石油集团川庆钻探工程有限公司 工具面状态的预测方法和系统、滑动定向钻井方法和系统
US12534994B2 (en) 2023-09-29 2026-01-27 Schlumberger Technology Corporation Drilling control

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