| 序号 | 专利名 | 申请号 | 申请日 | 公开（公告）号 | 公开（公告）日 | 发明人 |
|---|---|---|---|---|---|---|
| 141 | RIG CONTROL APPARATUS, SYSTEM, AND METHOD | US15834829 | 2017-12-07 | US20190178073A1 | 2019-06-13 | Scott Gilbert Boone |
| A rig control system according to which an automated sequence engine includes a sequence template module configured to provide a template that includes a plurality of data fields outlining operational steps and associated parameters to perform a drilling process, and a recipe learning module configured to generate a recipe for entry into the data fields. The recipe learning module is configured to retrieve a data set related to a drilling rig's performance of the drilling process to drill a wellbore segment, and to score the data set based on a result of the drilling rig's performance of the drilling process and/or a characteristic of the wellbore segment. In some embodiments, the recipe learning module is further configured to categorize the data set based on a characteristic of the drilling rig and/or the wellbore segment. The recipe is based on the data set, the scoring, the categorizing, or any combination thereof. | ||||||
| 142 | SYSTEMS AND METHODS FOR EARLY WELL KICK DETECTION | US15397247 | 2017-01-03 | US20180187498A1 | 2018-07-05 | Gerardo Alonso Sanchez Soto; Rafael Horschutz Nemoto |
| An early kick detection system including a kick detection computing device coupled to at least one sensor associated with a drilling system of a well. The kick detection computing device is configured to receive measurement data from the at least one sensor. The measurement data includes one or more kick indicators used to identify a well kick. The kick detection computing device generate an estimated value for each of the one or more kick indicators during simulated normal drilling conditions and simulated kick conditions. The kick detection computing device determines a deviation value and generates a signal based on the deviation value. The signal activates a green status, an amber warning, or a red alarm to indicate a kick detection status of the drilling system. | ||||||
| 143 | Resource production forecasting | US14332014 | 2014-07-15 | US10012748B2 | 2018-07-03 | Siddhartha Gupta; Franz Fuehrer |
| A method can include providing a trained neural network; providing a set of production values where the set includes, for example, a cumulative production value for an interval, an average production value for the interval, a first production value for the interval and a last production value for the interval; and predicting at least one production value for a subsequent interval based at least in part on the trained neural network and the provided set of production values. Various other apparatuses, systems, methods, etc., are also disclosed. | ||||||
| 144 | Life-time management of downhole tools and components | US14051353 | 2013-10-10 | US09857271B2 | 2018-01-02 | Dmitriy Dashevskiy; John D. MacPherson |
| Systems, methods and devices for evaluating a condition of a downhole component of a drillstring. Methods include estimating a value of a tool parameter of the component at at least one selected position on the drillstring; and using the estimated value to evaluate the condition of the downhole component. The estimating is done using a trained artificial neural network that receives information from at least one sensor that is positionally offset from the selected position. The method may further include creating a record representing information from estimated values of the tool parameter at the at least one selected position over time. The at least one selected position may include a plurality of positions, such as positions at intervals along the component, including substantially continuously along the component. | ||||||
| 145 | Determining drilling state for trajectory control | US14534119 | 2014-11-05 | US09850712B2 | 2017-12-26 | Junichi Sugiura |
| Methods are provided for determining the drilling state of a downhole tool and controlling the trajectory of the downhole tool in a wellbore during a drilling operation. One method may include identifying a drilling parameter indicative of the drilling state of the downhole tool in the wellbore. The method may also include determining the drilling state based on the identified drilling parameter. The identified drilling parameter may be obtained from a sensor communicatively coupled with a processor and disposed in the wellbore. The method may further include adjusting the operation of an integral controller based on the determined drilling state to control the trajectory of the downhole tool in the wellbore during the drilling operation. | ||||||
| 146 | AUTOMATED GEO-TARGET AND GEO-HAZARD NOTIFICATIONS FOR DRILLING SYSTEMS | US15343007 | 2016-11-03 | US20170122095A1 | 2017-05-04 | Peter W. Flanagan |
| A drilling management system can monitor the traversed path of a drill bit throughout active drilling at a drilling site and notify appropriate team members regarding a current status of the active drilling in real-time. For example, the drilling management system can notify team members when predetermined milestones have been met, when the drill bit is drifting off course from a target wellbore trajectory from a target horizon or target zone, or when the drill bit is in danger of running into a geo-hazard, such as a pre-existing wellbore, unpierced fault plane, lease boundary, etc. The drilling management system can maintain a depth model of the drilling site that identifies the target wellbore trajectory, a target zone based on one or more horizons from the depth model, and coordinates of known geo-hazards at the drilling sites. The drilling management system can also maintain a set of rules for each of the drilling sites that indicates when team members should be notified. | ||||||
| 147 | Optimization of dynamically changing downhole tool settings | US13154921 | 2011-06-07 | US09587478B2 | 2017-03-07 | David P. Moran; Stuart R. Oliver |
| A computer-assisted method for optimizing a drilling tool assembly, the method comprising defining a desired drilling plan; determining current drilling conditions; determining current drilling tool parameters of at least two drilling tool assembly components; analyzing the current drilling conditions and the current drilling tool parameters to define a base drilling condition; comparing the base drilling condition to the desired drilling plan; determining a drilling tool parameter to adjust to achieve the desired drilling plan; and adjusting at least one drilling tool parameter of at least one of the two drilling tool assembly components based on the comparing the base drilling condition to the desired drilling plan. | ||||||
| 148 | DRILLING APPARATUS | US15028699 | 2014-10-09 | US20160251901A1 | 2016-09-01 | Marian Wiercigroch |
| Provided is an apparatus for use in resonance enhanced drilling, which apparatus comprises a drilling module comprising a drill-bit, wherein the apparatus further comprises: a sensor for measuring one or more parameters relating to the interaction of the drill-bit and the material being drilled; and a sensor for measuring one or more motions of the drill-bit. | ||||||
| 149 | Managing a Wellsite Operation with a Proxy Model | US15025408 | 2013-10-18 | US20160230513A1 | 2016-08-11 | Jason D. Dykstra; Qiuying Gu |
| Techniques for managing a hydraulic fracturing operation include receiving a selection of a proxy model that represents a first principles model of a hydraulic fracturing operation, the proxy model including at least one property of a plurality of properties of the first principles model associated with the hydraulic fracturing operation; simulating the selected proxy model to generate a modeled output based on the property; and determining a value of a control setpoint for hydraulic fracturing operation equipment based on the modeled output. | ||||||
| 150 | ADAPTIVE DRILLING VIBRATION DIAGNOSTICS | US14920712 | 2015-10-22 | US20160115778A1 | 2016-04-28 | Eric van Oort; Theresa Baumgartner; Pradeepkumar Ashok |
| The disclosure relates to an adaptive system for diagnosing vibrations during drilling including a drilling assembly at least partially located in a wellbore, a sensor located in the wellbore, and a data processing unit. The drilling assembly may drill the wellbore. The sensor may detect high frequency data reflecting vibrations in the drilling assembly. The data processing unit may execute a classification model based on machine learning techniques which uses features extracted from the high frequency data to diagnose the type or intensity of a vibration or both in the drilling assembly. The disclosure further relates to an adaptive method of diagnosing vibrations during drilling by collecting high frequency data reflecting vibrations in a drilling assembly, extracting at least one feature from the high frequency data, and diagnosing the type of vibration using the at least one extracted feature and a classification model based on machine learning techniques. | ||||||
| 151 | Lift-gas optimization with choke control | US13253680 | 2011-10-05 | US09031674B2 | 2015-05-12 | Kashif Rashid; Suleyman Demirel; Benoit Couet |
| A method of optimizing production of wells using choke control includes generating, for each well, an intermediate solution to optimize the production of each well. The generating includes using an offline model that includes a mixed-integer nonlinear program solver and includes using production curves based on a choke state and a given wellhead pressure. The method further includes calculating, using a network model and the intermediate solution of each well, a current online wellhead pressure for each well. The method further includes setting the intermediate solution as a final solution based on determining that a difference between the current online wellhead pressure of each well and a prior online wellhead pressure of each well is less than a tolerance amount. The method further includes adjusting, using the final solution of each well, at least one operating parameter of the wells. | ||||||
| 152 | Neural net for use in drilling simulation | US13645562 | 2012-10-05 | US08954304B2 | 2015-02-10 | David P. Moran; Mark P. Frenzel; Roy Duncan |
| A method of optimizing a drilling tool assembly including inputting well data into an optimization system, the optimization system having an experience data set and an artificial neural network. The method further including comparing the well data to the experience data set and developing an initial drilling tool assembly based on the comparing the well data to the experience data, wherein the drilling tool assembly is developed using the artificial neural network. Additionally, the method including simulating the initial drilling tool assembly in the optimization system and creating result data in the optimization system based on the simulating. | ||||||
| 153 | Iterative drilling simulation process for enhanced economic decision making | US12179221 | 2008-07-24 | US08949098B2 | 2015-02-03 | William W. King |
| An iterative drilling simulation method and system for enhanced economic decision making includes obtaining characteristics of a rock column in a formation to be drilled, specifying characteristics of at least one drilling rig system; and iteratively simulating the drilling of a well bore in the formation. The method and system further produce an economic evaluation factor for each iteration of drilling simulation. Each iteration of drilling simulation is a function of the rock column and the characteristics of the at least one drilling rig system according to a prescribed drilling simulation model. | ||||||
| 154 | OPTIMIZATION WITH A CONTROL MECHANISM USING A MIXED-INTEGER NONLINEAR FORMULATION | US14174643 | 2014-02-06 | US20140156238A1 | 2014-06-05 | Kashif Rashid; Sulyman Demirel; Benoit Couet |
| A method of optimizing production of wells using choke control includes generating, for each well, an intermediate solution to optimize the production of each well. The generating includes using an offline model that includes a mixed-integer nonlinear program solver and includes using production curves based on a choke state and a given wellhead pressure. The method further includes calculating, using a network model and the intermediate solution of each well, a current online wellhead pressure for each well. The method further includes setting the intermediate solution as a final solution based on determining that a difference between the current online wellhead pressure of each well and a prior online wellhead pressure of each well is less than a tolerance amount. The method further includes adjusting, using the final solution of each well, at least one operating parameter of the wells. | ||||||
| 155 | ROCK FACIES PREDICTION IN NON-CORED WELLS FROM CORED WELLS | US13888013 | 2013-05-06 | US20140149041A1 | 2014-05-29 | Roger R. Sung; Yunsheng Li; Chuanyu Stephen Sun |
| Facies in wells in areas of a hydrocarbon reservoir are predicted or postulated. Artificial neural networks are utilized to build a training image based on rock phases which are described and interpreted using existing data obtained from certain wells in the reservoir, and also well log characteristics of those same wells for each rock facies. Well logs from which wells where no well core data has been collected are then analyzed against the training image and the rock facies in the non-cored wells are postulated. The cost and also the possibility of damage to the wells from extraction of the core rock during drilling are avoided. | ||||||
| 156 | Systems and Methods for Reducing Reservoir Simulator Model Run Time | US13990753 | 2010-11-30 | US20130338985A1 | 2013-12-19 | Alejandro Garcia; Jordani Rebeschini; Sergio Henrique Guerra de Sousa; Gerardo Mijares; Jose Antonio Rodriguez; Luigi Alfonso Saputelli; William Douglas Johnson |
| Systems and methods for reducing run time for a reservoir simulator model using a proxy model based on a neural network. | ||||||
| 157 | Methods, systems, and computer readable media for fast updating of oil and gas field production models with physical and proxy simulators | US11669928 | 2007-01-31 | US08504341B2 | 2013-08-06 | Alvin Stanley Cullick; William Douglas Johnson |
| Methods, systems, and computer readable media are provided for fast updating of oil and gas field production optimization using physical and proxy simulators. A base model of a reservoir, well, or a pipeline network is established in one or more physical simulators. A decision management system is used to define uncertain parameters for matching with observed data. A proxy model is used to fit the uncertain parameters to outputs of the physical simulators, determine sensitivities of the uncertain parameters, and compute correlations between the uncertain parameters and output data from the physical simulators. Parameters for which the sensitivities are below a threshold are eliminated. The decision management system validates parameters which are output from the proxy model in the simulators. The validated parameters are used to make production decisions. | ||||||
| 158 | Method of training neural network models and using same for drilling wellbores | US12265879 | 2008-11-06 | US08417495B2 | 2013-04-09 | Dmitriy Dashevskiy |
| A method of creating and using a neural network model for wellbore operations is disclosed. The method, in one aspect, may include defining a plurality of a wellbore parameter; calculating a plurality of output values of a tool operating parameter using the plurality of values of the wellbore parameter as input to a preexisting model; and obtaining a neural network model by using the plurality of values of the wellbore parameter and the calculated plurality of output values of the tool operating parameter. The neural network may be utilized for any suitable wellbore operation, including in conjunction with a drilling assembly for drilling a wellbore. | ||||||
| 159 | LIFT-GAS OPTIMIZATION WITH CHOKE CONTROL | US13253680 | 2011-10-05 | US20120095603A1 | 2012-04-19 | Kashif RASHID; Suleyman DEMIREL; Benoit COUET |
| A method of optimizing production of wells using choke control includes generating, for each well, an intermediate solution to optimize the production of each well. The generating includes using an offline model that includes a mixed-integer nonlinear program solver and includes using production curves based on a choke state and a given wellhead pressure. The method further includes calculating, using a network model and the intermediate solution of each well, a current online wellhead pressure for each well. The method further includes setting the intermediate solution as a final solution based on determining that a difference between the current online wellhead pressure of each well and a prior online wellhead pressure of each well is less than a tolerance amount. The method further includes adjusting, using the final solution of each well, at least one operating parameter of the wells. | ||||||
| 160 | Combining belief networks to generate expected outcome | US12940253 | 2010-11-05 | US08061440B2 | 2011-11-22 | Clinton D. Chapman; Charles Chen |
| A computer usable medium including computer usable program code for determining an oilfield parameter for a drilling operation. The computer usable program code when executed causing a processor to identify first decision factors and second decision factors about the drilling operation, where each of the first decision factors is contained within first nodes, and where each of the second decision factors is contained within second nodes, where the first and second nodes contain common nodes. The computer usable program code further causing the processor to associate the first nodes to create a first belief network and associate the second nodes to create a second belief network, associate the common nodes of the first belief network with the common nodes of the second belief network to form a multinet belief network, and generate at least one oilfield parameter from the multinet belief network. | ||||||
QQ群二维码
意见反馈
意见反馈