Abstract: Disclosed are methods and systems for: receiving raw geoscience or energy domain data associated with a resource site, such that the raw geoscience or energy domain data comprises a plurality of textual data and image data having a plurality of disparate file/document formats; receiving a user input associated with the raw geoscience or energy domain data; applying the raw geoscience and energy domain data and the user input to a configured embeddings model thereby generating one or more of: a text embedding associated with the raw geoscience or energy domain data, and an image embedding associated with the raw geoscience or energy domain data; implementing, based on the applying, one or more of: a semantic search computing operation and a classification or clustering computing operation associated with a multidimensional vector space; and generating a report, based at least on the semantic search/classification/clustering computing operation.

Abstract: A method for generating a response to a user query includes receiving a user query that involves user query text and/or one or more user query images. The method also includes converting the user query into a contextualized query using a multimodal retrieval-augmented generation (RAG) agent. The method also includes retrieving paths from a vector database in response to the contextualized query. The method also includes retrieving contents from a storage in response to the paths. The method also includes generating an answer to the user query based upon the contents.

Abstract: A method can include receiving input text associated with field operations at a field site that includes at least one well in fluid communication with a reservoir; assessing the input text with respect to one or more criteria to generate one or more chunks of text from the input text; directing the one or more chunks of text and a prompt to a large language model to generate corresponding output; and transforming the corresponding output into a graph structure, where the graph structure includes nodes and edges, and where each of the edges describes a relationship between a pair of the nodes.

Abstract: A method for creating a domain-specific benchmarking dataset for a domain-specific task in an oil and/or gas domain includes receiving input data. The method also includes receiving the domain-specific task that is related to the oil and/or gas domain. The method also includes receiving a prompt from a user. The prompt is received by a text or multimodal large language model (LLM). The method also includes generating a plurality of synthetic instruction-response pairs in response to the prompt based upon the input data and the domain-specific task. The synthetic instruction-response pairs are created by the text or multimodal LLM. The synthetic instruction-response pairs form at least part of the domain-specific benchmarking dataset.

Abstract: A method for using generative artificial intelligence to generate an answer in response to a natural language query that is directed to oil and gas exploration, drilling, and/or production includes receiving a plurality of documents. The method also includes splitting the documents into chunks. The method also includes generating a plurality of embeddings based upon the chunks. The method also includes storing the chunks and the embeddings in a vector database. The method also includes receiving a natural language query directed to oil and gas exploration, drilling, and/or production. The method also includes generating a query embedding based upon the natural language query. The method also includes retrieving a subset of the chunks based upon the query embedding. The method also includes generating an answer in response to the natural language query. The answer is based upon the natural language query and the subset of the chunks.

Abstract: Converting text-based requirements to a live prototype includes receiving text with a natural language string. The text includes a term for a component from a design language system and the natural language string describes at least a part of an interface. The method further includes generating, by a converter module, an output string from the text by: tokenizing the text to form a plurality of tokens that include a token for the term, tagging the token with a tag for the term, and applying a rule that uses the token and the tag. Applying the rule maps the token to the component of the design language system and writes the component into the output string. The method further includes generating the interface from the output string. The interface is made with the design language system and includes the component from the output string. The interface is presented with the component to a device.

Abstract: Converting text-based requirements to a live prototype includes receiving text with a natural language string. The text includes a term for a component from a design language system and the natural language string describes at least a part of an interface. The method further includes generating, by a converter module, an output string from the text by: tokenizing the text to form a plurality of tokens that include a token for the term, tagging the token with a tag for the term, and applying a rule that uses the token and the tag. Applying the rule maps the token to the component of the design language system and writes the component into the output string. The method further includes generating the interface from the output string. The interface is made with the design language system and includes the component from the output string. The interface is presented with the component to a device.