Abstract: A method for drilling a well includes prescribing a drill recipe with a drill analysis engine based on a field-specific model. The drill recipe identifies prescribed segments, mud flow parameters, and drill parameters. Each prescribed segment of the prescribed segments includes a start depth. The method further includes initiating drilling of the well based on the drill recipe; receiving a drill log in narrative text format; detecting a drill event inconsistent with the drill recipe by analyzing the drill log with a log analysis engine; prescribing an adjusted drill recipe with the drill analysis engine based on the field-specific model in response to the drill event, the adjusted drill recipe including adjusted prescribed segments; and drilling the well based on the adjusted drill recipe.

Abstract: A method for determining a drill event includes receiving a set of historic drill reports with annotations. The historic drill reports include a plurality of entries including multiple acronyms relating to a single drilling factor. A set of entries of the plurality of entries indicates an associated depth. The method further includes training a report analysis engine utilizing the historic drill reports and annotations; receiving a drill report associated with a well; and determining a drill event and associated depth utilizing the report analysis engine applied to the drill report.

Abstract: A system includes a field-specific model; a log analysis engine; a drill analysis engine; an interface; and computational circuitry in communication with the field-specific model, the log analysis engine, the drill analysis engine, and the interface. The computational circuitry is to: prescribe a drill recipe using the drill analysis engine based on the field-specific model, the drill recipe identifying prescribed segments, mud flow parameters, and drill parameters, each prescribed segment of the prescribed segments including an associated depth; receive a drill log in narrative text format via the interface; detect a drill event inconsistent with the drill recipe by analyzing the drill log with the log analysis engine; and prescribe an adjusted drill recipe using the drill analysis engine based on the field-specific model in response to the drill event, the adjusted drill recipe including adjusted prescribed segments.

Abstract: A method for drilling a well includes prescribing a drill recipe using a drill analysis engine based on a field-specific model. The drill recipe identifies prescribed segments. Each prescribed segment of the prescribed segments includes a start depth. The method further includes initiating drilling of the well based on the drill recipe; detecting a drill event inconsistent with the drill recipe; prescribing an adjusted drill recipe using the drill analysis engine based on the field-specific model in response to the drill event, the adjusted drill recipe including adjusted prescribed segments; and drilling the well based on the adjusted drill recipe.

Abstract: Systems and methods allow the use of algebra to optimize several queries at once by algebraically breaking them into pieces, interleaving them in the most efficient way and then computing the queries together. For instance, a user or application may have many queries to process. A computing device may handle each query sequentially. However, if the computing device handled the queries simultaneously and if they are presented at once, there are ways to algebraically optimize them together by interleaving the tasks required to execute each one and complete the entire batch more efficiently.

Abstract: The systems, methods, devices, and non-transitory media of the various embodiments provide query independent data identification. In various embodiments, query independent data identification may be used to facilitate data reuse. Query independent data identification may be accomplished using an algebraic expression hash (AEH) function to identify data in a graph or table for reuse based on its origin and what has been done to the data. Use of an AEH function may support a top down approach for identification of data reuse and may also facilitate faster searches using an AEH value. For example, a hash-based search of a universe of data sets may facilitate a top down approach to locate the maximal reuse first (as opposed to the last) and may be less sensitive to the size of the universe.

Abstract: The systems, methods, devices, and non-transitory media of the various embodiments enable query execution plan graphs to be compared to determine whether all or portions of two or more queries define data sets that are structurally equivalent. Two data sets may be structurally equivalent when each data set may be composed with a bijective relation that yields the other. In the various embodiments, when all or a portion of a first query that has been previously run defines a data set that is structurally equivalent to a data set defined by all or a portion of a second query that is to be run, the structure preserving transform may be applied to the corresponding portion of the second query to transform that portion of the second query into the corresponding portion of the first query, thereby allowing the results from previously running the first query to be reused.