WHAT'S NEW

LAS VIEWER & SPLICER BY USING CATGEOKU SOFTWARE

To the esteemed oil and gas professionals,

Imagine you have a stack of well data files (LAS files) from various sources. You need to analyze them, integrate them, and visualize them—all without the hassle of heavy-duty software or cumbersome processes. That's why we've developed a concise, powerful, and user-friendly web application, designed specifically for your needs.

Key Features: A Fast Solution for Log Analysis

1. A Direct and Efficient Interface

No more confusing menus. Our application offers an intuitive drag-and-drop interface. Simply drag and drop your LAS files, and the application gets to work. Minimal controls ensure you can focus on the data, not on navigation.

2. Smart LAS Data Management

This application can process one or more LAS files simultaneously, automatically identifying all curves (such as GR, RHOB, NPHI) and their associated metadata. This saves you from the tedious task of manual data parsing.

Integrated and Interactive Visualization

3. Fast Log Classification and Filtering

Forget manual searches. Our application intelligently classifies your logs into common petrophysical categories, such as Gamma Ray, Bulk Density, Resistivity, and more.If your log data does not fall into any classification, you can do it yourself in this tool.

4. Dynamic and Industry-Standard Plotting

Using an advanced visualization library, our application generates interactive log plots. You can view well logs with an accurate depth scale (Y-axis). The feature automatically applies a logarithmic scale for resistivity logs, ensuring data visualization aligns with standard industry practices. You can also zoom and pan to examine data details at specific depth intervals.

Splicing: Merging Data with Ease

5. Data Splicing and Consolidation

This is a real game-changer. Our application allows you to merge data from multiple LAS files into a single, cohesive file.

The process is simple:

  • Select the logs you want to merge.

  • Click "Process Splice".

  • The application will merge the log data into a single continuous dataset based on depth.

  • A progress bar monitors the process, especially for large files, providing visual feedback.

Once complete, you can immediately download the merged LAS file (spliced.las). The application also provides a CSV summary that outlines log availability in each file, helping you with data documentation and auditing.



YOUR WORKSPACE

Advanced LAS Viewer & Splicer

Advanced LAS Viewer & Splicer (Classification-Based)

Drag and Drop LAS files here
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GEOLOGICAL VALUE INTERPOLATION BY USING CATGEOKU SOFTWARE

The following is a summary of the steps for determining the value of data using the IDW method:
1. Determine the target point to be predicted and collect variable data from the sample points.
2. Calculate the distance between the target point and each sample. If using 3D coordinates (X, Y, Z), use Euclidean distance:
\[ d_i = \sqrt{(X_0 - X_i)^2 + (Y_0 - Y_i)^2 + (Z_0 - Z_i)^2} \]
3. Calculate the weight of each sample using:
4. Multiply the weight by the sample value, then sum up all the results. The power parameter $p$ is usually 2.
\[ w_i = \frac{\dfrac{1}{d_i^p}}{\sum_{j=1}^{n} \dfrac{1}{d_j^p}} \]
5. The sum is the estimated value at the target point, with automatically greater weight given to samples that are closer.


YOUR WORKSPACE







X: Y: Z:

Prediction Result:

Visualization:



Reference:
Sarita, R., Lepong, P., & Asmaidi. (2024). Analisis interpolasi inverse distance weighted (IDW) dan ordinary kriging (OK) untuk estimasi volume batubara di area Sebuku Kalimantan Selatan. Jurnal Geosains Kutai Basin, 7(1), 1–15. E-ISSN 2615-5176.


CONVERT MUDLOG LITHOLOGY DESCRIPTION FROM IMAGE TO EXCEL BY USING CATGEOKU SOFTWARE

Working with mudlog data in description form can be exhausting — writing every top, bottom, and lithology into Excel takes too much time. With our tool, you can instantly turn descriptions into structured, editable data (see Figure 1). No more manual typing, no more wasted hours.

👉 Faster
👉 Error-free
👉 Ready for analysis

Make your workflow smarter. Try our product today!

Figure 1. Step-by-step conversion of mudlog lithology descriptions from images to Excel


YOUR WORKSPACE

Drag & Drop Your Mudlog Lithology Description

Drop or click to upload mudlog image
Extraction Results:




COMPONENT STRESS ANALYSIS USING MOHR-COULOMB FAILURE CRITERIA AND MOHR CIRCLE THEORY WITH CATGEOKU SOFTWARE

Things to note before using this tool are: 
1. This tool aims to help determine the Mohr circle in relation to the failure envelope line and the value of the plane orientation in relation to the principal stress axis
2. Rock stability occurs when the Mohr circle does not touch the failure envelope line.
3. Rock conditions are fractured when approaching the failure envelope line.
4. Rock conditions are broken when crossing the failure
5. ΔPp is the difference between the final and initial pore pressure values 
6. This Mohr-Coulomb calculation has been corrected with: https://www.doitpoms.ac.uk/tlplib/metal-forming-1/problem2.php


Mohr–Coulomb Diagram (Full Circle, Scale X=Y)

YOUR WORKSPACE

Attention: Use the input format according to the placeholder. Unit: σ′₁ (Psi), σ′₃ (Psi), ΔPp (Psi), c (Psi), and φ (°).

Mohr–Coulomb diagram (Full circle, X=Y scale)







DETERMINE TOP OF OVERPRESSURE, KICK, AND STRESS REGIME BY USING CATGEOKU SOFTWARE

Before using these tools, there are several things that must be understood:
1. Normal Shale Porosity data can be obtained from uncompacted sonic logs, laboratory data, or literature data.
2. Biot's Coefficient data can be obtained from laboratory data or literature data.
3. ΔP is the difference between Pore Pressure and Hydrostatic Pressure values
4. Bulk Density data can be obtained from RHOB logs, but must first be converted to kg/m³.
5. Shale porosity data can be obtained from DT logs.
6. Data for validating overpressure can be obtained from MDT or DST.
7. Shmin data can be obtained from Mini-frac/leak-off tests (LOT, MDT, RFT) or borehole breakouts.
8. Shmax data is obtained by using geomechanical data and rock properties.


Pore Pressure, Hydrostatic & Overburden Stress vs Depth (CatGeoku)

YOUR WORKSPACE

Attention: Use the input format according to the placeholder. Units: TVD (m), BulkDensity (kg/m³), Shale Porosity (%), Mudweight (ppg), Pressure/output in Psi. Copy paste from your Excel.



Log Data (TVD (m), Bulk Density (kg/m³), Shale Porosity (%)):

Stress Data (TVD (m), Shmin (Psi), Shmax (Psi)) — Optional:

Validation Data (TVD (m), Pressure (Psi)) — Optional:

Mudweight Data (Depth (m), Mudweight (ppg)) — Optional:

Refference:
Zhang, Jon. (2013). Effective stress, porosity, velocity and abnormal pore pressure prediction accounting for compaction disequilibrium and unloading. Marine and Petroleum Geology. 45. 1-15.
Markou, Nikolaos & Papanastasiou, Panos. (2018). Petroleum geomechanics modelling in the Eastern Mediterranean basin: Analysis and application of fault stress mechanics. Oil & Gas Science and Technology. 73. 57.



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