A collection of Well Engineering tools
welleng aspires to be a collection of useful tools for Wells/Drilling Engineers, kicking off with a range of well trajectory tools.
- Generate survey listings and interpolation with minimum curvature
- Calculate well bore uncertainty data (utilizing either the ISCWSA MWD Rev5 models) - the coded error models are within 0.001% accuracy of the ISCWSA test data.
- Calculate well bore clearance and Separation Factors (SF)
welleng is fuelled by copious amounts of coffee, so if you wish to supercharge development please donate generously:
Maximum Curvature Method: added an alternative
Surveymethod for calculating a well trajectory from survey stations that add a more realistic (in terms of tortuosity) versus the traditional minimum curvature method. See this post for more details.
Modified Tortuosity Index: added a
Surveymethod for calculating a modified tortuosity index as described here.
Panel Plot: added the
Survey.figure()method to return plan and section plots.
Torque and Drag: added a simple
torque_dragmodule and an
architecturemodule for creating scenarios (well bore and simple strings) - see this post for instructions.
Vertical Section: this should have been included a long time ago, but finally a vertical section will be calculated if the
vertical_section_azimuthparameter is included in the
SurveyHeaderwhen initiating a
Surveyinstance. Otherwise, to return the vertical section for a given azimuth (e.g. 45 degrees), or to set the vertical section azimuth and add the vertical section data to the
Hello version 0.4: major version update to reflect all of the changes happening in the back end. If you have code that's built on previous versions of welleng then please lock that version in your env since likely it will require modifying to run with version 0.4 and higher.
Project Ahead: you can now project a survey from the last station to the bit or project to a target to see how to get back on track:
>>> node_bit = survey.project_to_bit(delta_md=9.0) >>> survey_to_target = survey.project_to_target(node_target, dls_design=3.0, delta_md=9.0)
Interpolate Survey on TVD Depth: new
surveyfunction for interpolating fixed TVD intervals along a welleng
Surveyinstance, e.g. to interpolate
surveyevery 10mTVD and return the interpolated survey as
>>> s_interp_tvd = survey.interpolate_survey_tvd(step=10)
OWSG Tool Error Models: the ISCWSA curated Rev 4 and Rev 5 tool models have been coded up and continue to honor the ISCWSA diagnostic data. The OWSG tool errors are experimental with the following status:
- Working: MWD, SRGM, _Fl, SAG, IFR1, IFR2, EMS
- Not Currently Working Correctly: AX, GYRO
The available error models can be listed with the following command:
World Magnetic Model Calculator: calculates magnetic field data from the World Magnetic Model if magnetic field strength is not provided with the survey data.
Import from Landmark .wbp files: using the
exchange.wbpmodule it's now possible to import .wbp files exported from Landmark's COMPASS or DecisionSpace software.
import welleng as we wp = we.exchange.wbp.load("demo.wbp") # import file survey = we.exchange.wbp.wbp_to_survey(wp, step=30) # convert to survey mesh = we.mesh.WellMesh(survey, method='circle') # convert to mesh we.visual.plot(mesh.mesh) # plot the mesh
Export to .wbp files (experimental): using the
exchange.wbpmodule, it's possible to convert a planned survey file into a list of turn points that can be exported to a .wbp file.
import welleng as we wp = we.exchange.wbp.WellPlan(survey) # convert Survey to WellPlan object doc = we.exchange.wbp.export(wp) # create a .wbp document we.exchange.wbp.save_to_file(doc, "demo.wbp") # save the document to file
Well Path Creation: the addition of the
connectormodule enables drilling well paths to be created simply by providing start and end locations (with some vector data like inclination and azimuth). No need to indicate how to connect the points, the module will figure that out itself.
Fast visualization of well trajectory meshes: addition of the
visualmodule for quick and simple viewing and QAQC of well meshes.
Mesh Based Collision Detection: the current method for determining the Separation Factor between wells is constrained by the frequency and location of survey stations or necessitates interpolation of survey stations in order to determine if Anti-Collision Rules have been violated. Meshing the well bore interpolates between survey stations and as such is a more reliable method for identifying potential well bore collisions, especially wth more sparse data sets.
More coming soon!
welleng uses a number of open source projects to work properly:
- trimesh - awesome library for loading and using triangular meshes.
- Flexible Collision Library - for fast collision detection.
- numpy - the fundamental package for scientific computing with Python.
- scipy - a Python-based ecosystem of open-source software for mathematics, science, and engineering.
- vedo - a python module for scientific visualization, analysis of 3D objects and point clouds based on VTK.
- magnetic-field-calculator - a Python API for the British Geological Survey magnetic field calculator.
A default, minimal welleng installation requires numpy and scipy which is sufficient for importing or generating trajectories with error models. Other libraries are optional depending on usage - most of welleng's functionality can be unlocked with the
easy install tag, but if you wish to use mesh collision functionality, then an advanced install is required using the
all install tag to get python-fcl, after first installing the compiled dependencies as described below.
You'll receive some
ImportError messages and a suggested install tag if you try to use functions for which the required dependencies are missing.
Default install with minimal dependencies:
pip install welleng
Easy install with most of the dependencies and no compiled dependencies:
pip install welleng[easy]
If you want to use the mesh collision detection method, then the compiled dependencies are required prior to installing all of the welleng dependencies.
Here's how to get the trickier dependencies manually installed on Ubuntu (further instructions can be found here):
sudo apt-get update sudo apt-get install libeigen3-dev libccd-dev octomap-tools
On a Mac you should be able to install the above with brew and on a Windows machine you'll probably have to build these libraries following the instruction in the link, but it's not too tricky. Once the above are installed, then it should be a simple:
pip install welleng[all]
For developers, the repository can be cloned and locally installed in your GitHub directory via your preferred Python env (the
dev branch is usuall a version or two ahead of the
git clone https://github.com/jonnymaserati/welleng.git cd welleng pip install -e .[all]
Make sure you include that
. in the final line (it's not a typo) as this ensures that any changes to your development version are immediately implemented on save.
!apt-get install -y xvfb x11-utils libeigen3-dev libccd-dev octomap-tools !pip install welleng[all]
Unfortunately the visualization doesn't work with colab (or rather I've not been able to embed a VTK object) so some further work is needed to view the results. However, the welleng engine can be used to generate data in the notebook. Test it out with the following code:
!pip install plotly jupyter-dash pint !pip install -U git+https://github.com/Kitware/ipyvtk-simple.git import welleng as we import plotly.graph_objects as go from jupyter_dash import JupyterDash # create a survey s = we.survey.Survey( md=[0., 500., 2000., 5000.], inc=[0., 0., 30., 90], azi=[0., 0., 30., 90.] ) # interpolate survey - generate points every 30 meters s_interp = s.interpolate_survey(step=30) # plot the results fig = go.Figure() fig.add_trace( go.Scatter3d( x=s_interp.x, y=s_interp.y, z=s_interp.z, mode='lines', line=dict( color='blue' ), name='survey_interpolated' ), ) fig.add_trace( go.Scatter3d( x=s.x, y=s.y, z=s.z, mode='markers', marker=dict( color='red' ), name='survey' ) ) fig.update_scenes(zaxis_autorange="reversed") fig.show()
Here's an example using
welleng to construct a couple of simple well trajectories with the
connector module, creating survey listings for the wells with well bore uncertainty data, using these surveys to create well bore meshes and finally printing the results and plotting the meshes with the closest lines and SF data.
import welleng as we from tabulate import tabulate # construct simple well paths print("Constructing wells...") connector_reference = we.survey.from_connections( we.connector.Connector( pos1=[0., 0., 0.], inc1=0., azi1=0., pos2=[-100., 0., 2000.], inc2=90, azi2=60, ), step=50 ) connector_offset = we.survey.from_connections( we.connector.Connector( pos1=[0., 0., 0.], inc1=0., azi1=225., pos2=[-280., -600., 2000.], inc2=90., azi2=270., ), step=50 ) # make survey objects and calculate the uncertainty covariances print("Making surveys...") sh_reference = we.survey.SurveyHeader( name="reference", azi_reference="grid" ) survey_reference = we.survey.Survey( md=connector_reference.md, inc=connector_reference.inc_deg, azi=connector_reference.azi_grid_deg, header=sh_reference, error_model='ISCWSA MWD Rev4' ) sh_offset = we.survey.SurveyHeader( name="offset", azi_reference="grid" ) survey_offset = we.survey.Survey( md=connector_offset.md, inc=connector_offset.inc_deg, azi=connector_offset.azi_grid_deg, start_nev=[100., 200., 0.], header=sh_offset, error_model='ISCWSA MWD Rev4' ) # generate mesh objects of the well paths print("Generating well meshes...") mesh_reference = we.mesh.WellMesh( survey_reference ) mesh_offset = we.mesh.WellMesh( survey_offset ) # determine clearances print("Setting up clearance models...") c = we.clearance.Clearance( survey_reference, survey_offset ) print("Calculating ISCWSA clearance...") clearance_ISCWSA = we.clearance.ISCWSA(c) print("Calculating mesh clearance...") clearance_mesh = we.clearance.MeshClearance(c, sigma=2.445) # tabulate the Separation Factor results and print them results = [ [md, sf0, sf1] for md, sf0, sf1 in zip(c.reference.md, clearance_ISCWSA.SF, clearance_mesh.SF) ] print("RESULTS\n-------") print(tabulate(results, headers=['md', 'SF_ISCWSA', 'SF_MESH'])) # get closest lines between wells lines = we.visual.get_lines(clearance_mesh) # plot the result we.visual.plot( [mesh_reference.mesh, mesh_offset.mesh], # list of meshes names=['reference', 'offset'], # list of names colors=['red', 'blue'], # list of colors lines=lines ) print("Done!")
This results in a quick, interactive visualization of the well meshes that's great for QAQC. What's interesting about these results is that the ISCWSA method does not explicitly detect a collision in this scenario wheras the mesh method does.
For more examples, including how to build a well trajectory by joining up a series of sections created with the
welleng.connector module (see pic below), check out the examples and follow the jonnymaserati blog.
Well trajectory generated by build_a_well_from_sections.py
- Add a
Targetclass to see what you're aiming for - in progress
- Generate a scene of offset wells to enable fast screening of collision risks (e.g. hundreds of wells in seconds)
- WebApp for those that just want answers
- Add a
unitsmodule to handle any units system - in progress
It's possible to generate data for visualizing well trajectories with welleng, as can be seen with the rendered scenes below. ISCWSA Standard Set of Well Paths
The ISCWSA standard set of well paths for evaluating clearance scenarios have been rendered in blender above. See the examples for the code used to generate a volve scene, extracting the data from the volve EDM.xml file.
Please note the terms of the license. Although this software endeavors to be accurate, it should not be used as is for real wells. If you want a production version or wish to develop this software for a particular application, then please get in touch with jonnycorcutt, but the intent of this library is to assist development.
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