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We have used the new hierarchical carbonate reservoir benchmarking case study created by Costa Gomes J, Geiger S, Arnold D to be used for reservoir characterization, uncertainty quantification and history matching.
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We have used the new hierarchical carbonate reservoir benchmarking case study created by Costa Gomes J, Geiger S, Arnold D to be used for reservoir characterization, uncertainty quantification and history matching.
Work repository is work in progress.
There is a Geolog project that uses the same workflow and data that is also available at the following link:
We have added a new TensorFlow example to our workflow to estimate permeability in the:
4_Chartbook_Porosity_Optimized-Lithology_Perm_Thomeer_Saturations_ver3-Lasio_with_FWL_search-implement_Optimization-Illite_another_Scipy_optimization_method-newperm_TF.ipynb
notebook, but the TensorFlow permeability estimations are not as good as kNN, and extremely slow to run with 1000 epochs unless working with GPU. Even on the Mac with M1 Pro chip, it took nearly 9 minutes to run the TensorFlow training.
There is another TensorFlow permeability estimation example that is a bit more straighforward:
5_Chartbook_Porosity_Optimized-Lithology_Perm_Thomeer_Saturations_ver3-Lasio_with_FWL_search-implement_Optimization-Illite_another_Scipy_optimization_method-MyNewPerm-NoTrain.ipynb
This method has been pre-trained using 200 epochs. The notebook that does the training is called:
5_Chartbook_Porosity_Optimized-Lithology_Perm_Thomeer_Saturations_ver3-Lasio_with_FWL_search-implement_Optimization-Illite_another_Scipy_optimization_method-MyNewPerm.ipynb
With this notebook we use our own normalized features (PHIT, RHOB and GR) and label (log10(Core_Perm)) vs. the 'black box' methods that are employed with some of the python packages. This way we can normalize and de-normalize as necessar knowing exactly how we performed the work.
We have added a new test Characterization Notebook that uses Panel Widgets to select the well and well log analysis parameters:
3test_Chartbook_Porosity_Optimized-Lithology_Perm_Thomeer_Saturations_ver3-Lasio_with_FWL_search-implement_Optimization-Illite_another_Scipy_optimization_method-newperm.ipynb
It is not totally straight forward, but please follow the directions in the Notebook:
- Intitialize the entire Notebook in the beginning by running then entire Notebook first
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We have developed a comprehensive Petrophysical Characterization Workflow for a carbonate field, and to demonstrate it, we have created a Jupyter Notebook on GitHub. Our workflow comprises several steps, and we have used chatGPT3 to refine our python coding.
73/100 healthFor our 3*.ipynb characterization notebooks we have added Andy McDonald's method to export las files for our results. The link to Andy's YouTube presentation is given in the notebook. Watch Andy's YouTube presentation on this:
We added a Dynamic data analysis of Costa Field using python Altair where we can select different wells in the field and view the production and BHP's from these wells. We are now trying to add the structure map as a base map in Altair for our Altair Field Maps. Altair is fairly flexible, but making a pie plot for each well of the production and adding basemap images has not yet been fully implemented in Altair.
Costa_Oil_Production.ipynb
According to Costa(1):
This work presents a new open-source carbonate reservoir case study, the COSTA model, that uniquely considers significant uncertainties inherent to carbonate reservoirs, providing a far more challenging and realistic benchmarking test for a range of geo-energy applications. The COSTA field is large, with many wells and large associated volumes. The dataset embeds many interacting geological and petrophysical uncertainties in an ensemble of model concepts with realistic geological and model complexity levels and varying production profiles. The resulting number of different models and long run times creates a more demanding computational challenge than current benchmarking models.
The COSTA model takes inspiration from the shelf-to-basin geological setting of the Upper Kharaib Member (Early Cretaceous), one of the most prolific aggradational parasequence carbonate formations sets in the world. The dataset is based on 43 wells and the corresponding fully anonymized data from the north-eastern part of the Rub Al Khali basin, a sub-basin of the wider Arabian Basin. Our model encapsulates the large-scale geological setting and reservoir heterogeneities found across the shelf-to-basin profile, into one single model, for geological modelling and reservoir simulation studies.
The result of this research is a semi-synthetic but geologically realistic suite of carbonate reservoir models that capture a wide range of geological, petrophysical, and geomodelling uncertainties and that can be history-matched against an undisclosed, synthetic 'truth case'. The models and dataset are made available as open-source to analyze several issues related to testing new numerical algorithms for geological modelling, uncertainty quantification, reservoir simulation, history matching, optimization and machine learning.
In this GitHub repository we have used a new, comprehensive reservoir characterization database from Costa, Geiger and Arnold(1) We employed all the available well logs, Routine Core Analysis (RCA) and Special Core Analysis (SCAL and implemented our normal carbonate reservoir characterization workflow as discussed by Phillips (2). For this repository we did not use the 3D static or dynamic models, but we did use the time-series dynamic production and formation pressure data by well in Spotfire to better understand the dynamic aspects of the reservoir too. This is a rich dataset that needs to be explored further, more than what is presented in the scope of this project.
The RCA and SCAL core data as well as the 17 well logs provided as las files were used extensively in our Petrophysical evaluation. These data are all in a Geolog project that is included in this repository. The following layout depth plot display shows a typical well from the field and the analysis performed on each well.
The process used in this repository follows a tried and proven workflow as described by Phillips et al. (2) in the characterization of an Arab D carbonate reservoirs from Saudi Arabia. Core Porosity, Permeability, Petrophysical Rock Types (PRT), Capillary Pressure and capillary pressure-based saturations are all estimated or calculated within this workflow. Just as in previous studies, the core analysis database was used extensively as the foundation and calibration to these petrophysical interpretations.
Costa provided 110 High Pressure Mercury Injection (HPMI) core samples with RCA Porosity and Permeability. We performed Thomeer Parameter Analysis on each HPMI sample to establish our own, reservoir specific Thomeer Parameter Capillary Pressure core database used to mode saturations. In Geolog we fit the Thomeer hyperbola to all 110 HPMI samples and established the Thomeer parameters for each sample. We used python loglan code to load the High-Pressure Mercury Injection (HPMI) Core data directly from the Costa SCAL dataset into a Geolog Well and then modeled the HPMI data using the Thomeer parameters fitting the Thomeer hyperbola to each HPMI sample. This portion of the Geolog project serves an example as to how to build your own reservoir-specific core calibration database for their own Reservoir Characterization studies.
The following workflow and processing are suggested to interrogate, process, interpret and model the petrophysical properties for this benchmark carbonate reservoir using Costa's RCA and High-Pressure Mercury Injection core database as calibration. The workflow consists of the following steps:
Normally we would run MultiMin for a solid log analysis model using the typical minerals found in this reservoir; Limestone, Dolomite, and Illite. With MultiMin we always use environmentally corrected log data, calculating the uncertainties for each log curve employed in the analysis. However, to make this dataset more universal, we have employed python to perform our deterministic log analysis of the 17 HW wells. This code is provided in the Geolog project. We received these data as las files with a text file of log header data including X,Y locations, KB elevation and TD. There were no directional survey data provided, so at this point we are assuming these wells to be vertical, and TVDss was calculated accordingly.
Use available core data from the representative reservoir/field to build a petrophysical model to estimate permeability for all wells in field using Geolog's Facimage. Our plan is to include a python loglan too using kNN with normalized input data that is weighted by Euclidean distances for each of the nearest neighbors to estimate permeability for each well.
Using the kNN estimated permeability and calculated Total Porosity (PHIT) from our log analysis, we queried Costa's core database to predict the following Petrophysical results:
Thomeer Capillary Pressure parameters (Pdi, Gi and BVocci) for each pore system, i, over the entire reservoir interval for Capillary Pressure-based saturations
Estimate the most dominant pore throat diameter at each level in the well calculated from the Thomeer parameters G and Pd. This exact mode of the pore throat distribution is calculated using the following equation:
*Mode of Pore Throat Distribution (microns) = exp(-1.15 * G1) * (214/Pd1)*
This exact Mode of the Pore Throat Distribution (PTD) is what Winland is trying to accomplish with r35 and Amaefule with FZI; however, they are not exact. The Mode of the PTD calculated from the Buiting equation is taken directly from the Capillary Pressure data and is the exact mode that correlates to other petrophysical parameters extremely well.
We used this mode to partition our data into Petrophysical Rock Types. If the Mode > 2 microns, then the PRT was a Macro porous rock. If the Mode > 0.1 micron and less that 2 microns, then this was Meso porous rock. If the Mode < 0.1 microns, then this rock was Micro porous rock.
We did perform a Free Water Level (FWL) search for each well. We use the Thomeer parameters to model Capillary Pressure based saturations as shown below:
We vary the FWL from the highest potential FWL elevation down to the lowest potential FWL elevation for the reservoir and pick the FWL where the error between the BVO on logs vs. Capillary Pressure are at a minimum.
This is an example of the single well output from a typical FWL search. The FWL search was performed on all wells in the study.
Not all wells are useful in establishing the FWL. A few of the wells were too high where all we really were estimating was the Base of Reservoir (BOR