## Data Download

Here, we provide the data of our publications for free download. You may use them for your own research by kindly citing the corresponding publication.

If you have any question, feel free to contact us.

### Well-to-Well Correlation and Identifying Lithological Boundaries by Principal Component Analysis of Well-logs

**Citation:** If you use this package in any publications please help us by citing the following article:

*Karimi, A. M., Sadeghnejad, S., Rezghi, M. (2021). Well-to-Well Correlation and Identifying Lithological Boundaries by Principal Component Analysis of Well-logs. Computers and Geosciences (in press).*

**Model Description:**

Various automated methods were introduced to accelerate well-to-well correlation; however, most of them use single well-log data. As each well-log contains specific information of rock and fluid properties, the simultaneous use of various well-logs can enhance the correlation accuracy. We extend an automatic well-to-well correlation approach from the literature to use the benefits of various well-logs by applying principal component analysis on multiple well-logs of a carbonate reservoir. The code can extract various features (i.e., mean, coefficient of variation, maximum to minimum ratio, trend angle, and fractal dimension) from a reference well and examin them across observation wells. The energy of principal components can also be evaluated to determine the appropriate number of principal components. The code can examine three different scenarios of applying principal component analysis and determine the best methodology for well-to-well correlation. In the first scenario, the principal component analysis reduces the dependency of statistical attributes extracted from a single well-log. In the 2nd model, principal component analysis is applied on multiple well-logs to extract their features (Scenario II). Finally, it is checked whether principal component analysis can be applied at multiple steps (Scenario III). The analysis of variance and Tukey are used to compare the accuracy of the scenarios.

For more information read the paper.

The code can be downloaded from here.

### Acid pre-flushing evaluation before pH-sensitive microgel treatment in carbonate reservoirs: Experimental and numerical approach

**Citation:** If you use this package in any publications please help us by citing the following article:

*Koochakzadeh, A., Younesian-Farid, H., & Sadeghnejad, S. (2021). Acid pre-flushing evaluation before pH-sensitive microgel treatment in carbonate reservoirs: Experimental and numerical approach. Fuel, 297, 120670.*

**Model Description:**

The compositional reactive flow model (advection-diffusion-reaction) is developed in Python and can analyse the behaviour of acid flooding experiments in porous media. This model can consider the dominant reactions that have a determinative effect on the dissolution of calcite and can predict the final equilibrium condition of the system. These reactions include acid ionization, calcite dissolution reactions, and the equilibrium reactions of the Ca2+ ion with other species.

In our numerical model, reactions are divided into two categories; 1- equilibrium reactions between species that are in the aqueous phase, and 2- the reactions of aqueous species with rock minerals. In addition, the species are divided into two categories of the primary and the secondary species. The only difference between the primary and the secondary species is the way that their concentration is calculated during the simulation which is explained in the following.

Core and fluid characteristics, flooding conditions, initial concentration of species, the kinetics of heterogeneous reactions, and the equilibrium constants of homogeneous reactions are the primary inputs of this developed model.

The mesh-cantered method was used for meshing the domain and 50 grid blocks were considered in the system. The upwind discretization scheme was used for discretizing the advection term to increase the numerical stability and to decrease the convergence problem.

For more information read the paper.

The code can be downloaded from here.

### Multi-scale reconstruction of vuggy carbonates by pore network modeling and image-based technique

**Citation:** If you use this package in any publications please help us by citing the following article:

*Sadeghnejad, S., & Gostick, J. (2020) Multi-scale reconstruction of vuggy carbonates by pore network modeling and image-based technique. SPE Journal, 25 (1), 253-267.*

**Model Description:**

To reconstruct a bi-modal vuggy porous medium, an algorithm based on coupling the PNM approach with image-based network techniques was implemented. A lattice-based network image (LNI) of a vugular network along with a conventional PNM was used to evaluate the network properties in parallel. LNI is a lattice network that consists of the secondary porosity (i.e., vugs) of the network. The LNI is laid on top of the base PNM (i.e., matrix porosity) which contains the microporous structure of the porous medium. Implementing such a twin LNI-PNM approach has considerable advantages. For example, the connection among the vuggy pores of the LNI with other micro-pores of the PNM can properly and efficiently be calculated (e.g., petrophysical properties of overlapping vugs). Moreover, big networks consisting of many million pores and vugs can be feasibly reproduced by using such a technique. In the first step, the microporous network and vug properties are defined as inputs. The base network (i.e., micro-porous) properties include network size, number of pores, throats, coordination number, the size distribution of pores and throats, etc. The vug properties consist of the total number of vugs in the network and vug diameter distribution. Based on the input data, the base PNM and its twin LNI are generated. The base PNM consists of only conventional micro-pores with a predefined pore size distribution.

For more information read the paper.

The code can be downloaded from here.

### Dual-scale pore network reconstruction of vugular carbonates using multi-scale imaging techniques

**Citation:** If you use this package in any publications please help us by citing the following article:

*Moslemipour, A., Sadeghnejad, S. (2021) Dual-Scale Pore Network Reconstruction of Vugular Carbonates using Multi-Scale Imaging Techniques. Advances in Water Resources, 147, January 2021, 103795.*

**Model Description:**

In this study, a dual-scale PNM is implemented to reconstruct the behavior of a vuggy carbonate sample. The rock sample is CT scanned at two different scales. At the macro-scale (i.e., vugular-network), a medical-CT scanner is used to image the rock sample at the resolution of 100 μm. The rock is also imaged by a micro-CT scanner at the resolution of 0.75 μm to extract the micro-scale properties (i.e., micro-network). The networks of both scales are extracted by two network extraction algorithms, and the results compared together. Then, a **stochastically equivalent network** based on the extracted micro-network properties is generated with a larger field of view (FOV). Then, vugs are randomly added to the reconstructed micro-network based on the properties of the macro-scale CT images. The result is a **dual-scale unstructured irregular PNM**. This modeling approach can efficiently preserve the vug-to-vug and vug-to-pore connectivity of overlapping vugs.

For more information read the paper.

The code can be downloaded from here.