Part 1 Importing Well Directional Survey Data

1.2 Field Mapping

After a csv file is uploaded, the mapping tool will guide a user to map the columns in the original csv file to ComboCurve Fields. The correct units need to be used to ensure correct calculation results subsequently:

1. Measured Depth (FT): foot
2. True Vertical Depth (FT): foot
3. Azimuth: degree
4. Inclination: degree
5. Deviation NS (FT): foot
6. Deviation EW (FT): foot
7. Latitude: degree
8. Longitude: degree
   

It is also worth noting that if deviation EW/deviation NS are imported, a user needs to make sure the correct positive/negative sign is included, which means ”+” for E/N and “-” for W/S. A reference datum that was used in the directional survey (“NAD83”, “NAD27” or “WGS84”) should also be selected.

1. True Vertical Depth, deviation NS, deviation EW, latitude, longitude are calculated along the wellbore trajectory following steps below:
   
1. Minimum curvature method
   
1. Minimum curvature is a commonly used method to calculate deviations and TVD from the measured data for well directional survey. The formula can be found here.
   
2. The input of this algorithm is [measuredDepth, azimuth, inclination] and the output is [trueVerticalDepth, deviationEW, deviationNS].The implementation was done in the wellpathy package and the code can be found here. It has been tested by QA to ensure the calculated results conform with the vendor reported data.
   
2. Projection of the well head location (LAT, LONG) on XY grid
   
1. As the calculated values from last step are referenced to the well head, the absolute location of the well head is needed to get the absolute locations of the entire wellbore. Functions from a python package pyproj are used to project a well head location in (latitude, longitude) to (X, Y) 2D map. The projection involves
   
1. getting the corresponding geodetic coordinate system for the specified location using pyproj.database.query_utm_crs_info and pyproj.crs.CRS
   
2. instantiate a pyproj.Transformer that transforms from geodetic CRS to projected CRS
   
3. Using the transformer to perform the projection
   
4. Apply unit conversion to convert to FOOT.
   
3. Determine the XY coordinates along the wellbore
   
1. The absolute XY coordinates of all points in the directional survey can then be obtained by (well_head_X + deviationEW, well_head_Y + deviationNS)
   
4. Transform XY coordinates to (LAT, LONG) for points on wellbore
   
1. The absolute coordinates can then be transformed to (latitude, longitude) by instantiating an inverse transformer as opposed to step b. 
   

1.3 MongoDB data schema 

                                                                   

Figure 1  a) SHL to SHL; b) midpoint-to-midpoint; c) midpoint to closest

 

1. SHL to SHL is the well head distance. It is the onldistance that can be calculated when there is only surface location. Well head distances are straightforward as they are point-to-point, namely
   
1. High efficiency is necessary to shorten the wait time for the users. In V32, we make use of two methods to calculate point to point distance:
   
1. use vectorized method to get the distance between one point and all the other points when the total number of points involved is less than 500;
   
2. use KDTree method from scipy.spatial when the total number of points involved is equal or larger than 500.
   
2. In rare cases, some wells have the same surface locations. We will neglect the zero distances and search for the minimum from the non-zero distances. If there is no well of different surface location from the target well, then zero distance is reported.
   
2. Mid-point to mid-point is the distance between the midpoint of well lateral sections. To determine the midpoint on the wellbore, ComboCurve uses the following method:
   
1. Find the point where inclination value > 85 degrees as the well heel;
   
2. When inclination is not available, we calculate the derivative of true vertical depth(TVD) with respect to measured depth (MD) and use d(TVD)/d(MD) < 0.2 as the threshold to mark where the lateral section starts;
   
3. Find the well toe as the last point in the well trajectory;
   
4. Mid-point is the point where its X is closest to (x_heel + x_toe)/2
   
3. Mid-point to closest is a bit more complicated than a) and b). It is valuable as it searches for the nearest well from the mid-point of the target well and is more representative of how close the target well is to other wells. The point to line distance, as shown in the figure |PQ| is determined by the following:
   
1. Use the lateral section points,  compute the centroid of points and perform singular value decomposition (use numpy.linalg.svd(data - centroid)) to get the line vector of the fitted straight line.
   
2. Determine if there is a projection from the target well midpoint on another well. As shown in the figure, well 1 midpoint cannot project on well 2 or 3. This check is done by the following:
   

 If well 1 can be projected on well 2.

 

                 

                        3. Find two points A and B on all the fitted lines. The projection from midpoint P to the line AB is the modulus of vector PQ. Vector                               PQ can be determined by the equation shown below. As PQ is calculated, the coordinate of the projected point Q is then                               known and distance between PQ on XY plane and Z plane can be obtained.

 

                                                     

1. Same zone: examine the distance of the selected well to the filtered wells in the same landing zone. Wells that don’t have the “landing zone” information will be neglected when using this option. For efficient calculations of point to point calculations, a mask is generated for each target zone. If the total number of wells is less than 1000, then a vectorized method with masking is used. Otherwise, all the wells involved in the calculation are grouped by their landing zones. For each group, if the total number of wells is larger than 500, then KDTree method is used. Otherwise the vectorized method is used with the corresponding landing zone mask.

2.1.3 (optional) CRS (reference link)

1. Reference datum: A modeled version of the shape of the Earth which defines the origin used to place the coordinate system in space. Different locations on the earth can use different ellipsoids to model the earth. The most used datum in North America includes WGS84, NAD27 and NAD83.
   

1. Units: The units used to define the grid along the x, y (and z) axis, e.g. foot or meter.
   
2. Projection Information: The mathematical equation used to flatten objects that are on a round surface (e.g. the Earth) so you can view them on a flat surface.

 

2.2 Run well spacing request

2.2.1 ‘filters/lightFilterWellsIds’ payload example

2.2.2 ‘well-spacing/validate’ payload example

2.2.3 ‘well-spacing/run’ payload example

 

2.3 Handling run well spacing request

2.3.1 (internal-api) “well-spacing/validate”

WellSpacingValidationService in internal-api handles checking the data availability before a run well spacing request is sent to python-api. Depending on the configuration selected, surface location, landing zone and directional survey data availability of the selected and filtered wells are checked by calling the database. This includes the following checking criteria:

1. Surface location and elevation are always needed;
   
2. Directional survey is checked when the selected distance type is mid-point related.
   
3. Landing zone is checked when the selected zone type is “Same zone”
   

Well spacing configuration		Well headers
Zone type	Distance type	Surface latitude, surface longitude, elevation	Landing zone	Directional survey
Any	SHL-to-SHL	✔
Any	Mid-to-mid	✔		✔
Any	mid-to-closest	✔		✔
Same	SHL-to-SHL	✔	✔
Same	Mid-to-mid	✔	✔	✔
Same	mid-to-closest	✔	✔	✔

   

If the filtered wells have missing data, the data availability is summarized and present to  the user. The user is given the choice of whether to proceed with the calculation and acknowledge that the wells with missing data will be ignored. If none of the selected wells meets the minimum data requirements, a warning message is displayed and calculation  won’t be able to proceed.

2.3.1 (internal-api) “well-spacing/run”

pythonService in the internal-api calls the python-api endpoint “/well-spacing/calculating-well-spacing”

2.3.2 (python-api) “/well-spacing/calculating-well-spacing”

This endpoint makes a function call of the calculate_well_spacing method in the WellSpacingService Class. A flowchart is shown at the end of this document to explain the algorithm flow and the functions called in the process.

The following well headers are updated after this function call returns:

1. Knowing the workflow
   
2. Avoiding mistakes when uploading directional survey data
   
3. Understanding what’s been calculated for different well spacing calculation configurations.
   
4. Knowing what is a CRS and how to choose the correct CRS to achieve best accuracy.
   
5. Knowing where to find the calculation results in ComboCurve.

1. The data storage format in database
   
2. How request is sent to internal-api and python-api, what are the services used
   
3. Algorithm used to achieve better performance for distance calculations.
   
4. How different distance configurations are handled by python
   

Appendix: Flowchart of distance calculation in python-CC