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README.md
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# Model Card for Model ID
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### Model Description
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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### Framework versions
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# Model Card for Model ID
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This model is fine-tuned on content from the content in Petrolumn Engnieer journal, enhancing its performance in logging and perforation tasks related.
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### Model Description
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-**Model Name**: Mistral-7B-Logging-Perforation-v1.0
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## Uses
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## Training Details
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### Training Data
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Paper text from SPE journal
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### Evaluation
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Question:
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Name 3 new technologies in Logging and Perforation
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Answer:
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`GPT-4`: "
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Here are three succinct new technologies in logging and perforation:
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1. Advanced Logging Sensors: Incorporating spectral logging and high-definition imaging for detailed analysis.
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2. Real-time Data Transmission: Digital tools transmit data instantly for immediate analysis.
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3.Engineered Perforating Systems: Utilizing shaped charge and precision systems for optimal wellbore perforation."
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`Pre-trained Mistral-7B`: "
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1. Electronic logging devices (ELDs) are electronic systems that record a driver's hours of service (HOS) information, including the date, time, location, engine hours, vehicle miles driven, and other data. ELDs help ensure compliance with HOS regulations by automatically recording driving time and preventing drivers from falsifying their logs.
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3. Perforating guns are specialized tools used to create perforations in wellbores during drilling operations. These guns use high-pressure water or air jets to create small holes in the rock formation, allowing for the injection of fluids such as cement or drilling mud into the formation.
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5. Smart perforators are advanced perforating tools that can be remotely controlled and monitored using telemetry technology. They allow operators to adjust the firing sequence and depth of penetration in real-time, improving accuracy and reducing the risk of damage to the wellbore.
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`Mistral-7B-Geoscience-v0.1`: "
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The three new technologies are:
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1. Distributed fiber-optic sensing (DFOS) technology for real-time monitoring of wellbore conditions during hydraulic fracturing operations. This technology uses optical fibers embedded in the wellbore to measure temperature, pressure, strain, and other parameters that can provide insights into the behavior of the rock formation and the effectiveness of the hydraulic fracturing process.
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3. High-pressure perforating technology for creating larger and more uniform perforations in hard formations. This technology involves using high-pressure nitrogen or carbon dioxide gas to create larger perforations with less damage to the surrounding rock.
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5. Smart completion systems for optimizing hydraulic fracturing treatments. These systems use sensors and actuators to monitor and control the flow of fluids and proppants into the wellbore during hydraulic fracturing. They allow for real-time adjustments to be made to the treatment based on downhole measurements, resulting in more efficient and effective fracturing treatments.
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"
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