Plunger Style Optimization by Machine Learning

Oil and gas wells often need artificial lift technologies to help extract reservoir fluids as the wells age and the reservoir pressure decreases–among those technologies is plunger lift. Plungers are metal cylinders that fit snug inside the tubing in which they are still able to slide freely. Plungers are often used with gas lift: they get pushed up and down the tubing like a piston to unload all the fluids by periodically shut in and open up the well. Many plungers contain a one-way valve that enables them to fall through flow easily and rise to the surface with a seal to prevent fluid slippage. There are many styles of plungers based on their weight, fall speed, and embedded one-way valves’ mechanism, and they should be used in different kinds of wells. Plungers can be grouped into five major different categories. This project utilizes machine learning and data analytics to predict what the most optimal type of plungers is for a given well in order to reduce gas injection and maximize liquid production. To accomplish this project, more than two million rows of raw plunger lift production data were queried using SQL, a database query language, then pivoted, cleaned, and turned into a data table with approximately two hundred twenty thousand rows. The data came from about 900 wells with a time span of more than 400 days of production. Utilizing the python package Scikit Learn's random forest regressor, five separate machine learning models were trained, tuned, and cross-validated to predict the daily revenue and/or efficiency of a well if it were to run with the corresponding style of the plunger. The models were able to achieve about .85-.90 accuracy scores. On the other hand, a data visualization guide was built to visualize all the past plunger operation history to analyze the efficiency of a type of plunger; it also acts as an educational tool for operators to study the behaviors of different plungers in various wells. The results of this project were achieved by field testing. Given that there was a time constraint, approximately ten wells were tested with eight of them showing revenue and/or efficiency improvements of 5-10%. More testing, tuning, and data gathering need to be done in the future to improve the project to apply at a larger scale.

Frameworks for Querying Databases Using Natural Language

A natural language interface is useful for a wide range of users to retrieve their desired information from databases without requiring prior knowledge of database query language such as SQL. The advent of user-friendly technologies, such as speech-enabled interfaces, have revived the use of natural language technology for querying databases; however, the most relevant and last work presenting state of the art was published back in 2013 and does not encompass several advancements. In this paper, the authors have reviewed 47 frameworks that have been developed during the last decade and categorized the SQL and NoSQL-based frameworks. Furthermore, the analysis of these frameworks is presented on the basis of criteria such as supporting language, scheme of heuristic rules, interoperability support, scope of the dataset, and overall performance score. The study concludes that the majority of frameworks focus on translating natural language queries to SQL and translates English language text to queries.

The history and recent advances of Natural Language Interfaces for Databases Querying

Databases have been always the most important topic in the study of information systems, and an indispensable tool in all information management systems. However, the extraction of information stored in these databases is generally carried out using queries expressed in a computer language, such as SQL (Structured Query Language). This generally has the effect of limiting the number of potential users, in particular non-expert database users who must know the database structure to write such requests. One solution to this problem is to use Natural Language Interface (NLI), to communicate with the database, which is the easiest way to get information. So, the appearance of Natural Language Interfaces for Databases (NLIDB) is becoming a real need and an ambitious goal to translate the user’s query given in Natural Language (NL) into the corresponding one in Database Query Language (DBQL). This article provides an overview of the state of the art of Natural Language Interfaces as well as their architecture. Also, it summarizes the main recent advances on the task of Natural Language Interfaces for databases.

Query Lifting

Language-integrated query based on comprehension syntax is a powerful technique for safe database programming, and provides a basis for advanced techniques such as query shredding or query flattening that allow efficient programming with complex nested collections. However, the foundations of these techniques are lacking: although SQL, the most widely-used database query language, supports heterogeneous queries that mix set and multiset semantics, these important capabilities are not supported by known correctness results or implementations that assume homogeneous collections. In this paper we study language-integrated query for a heterogeneous query language $$\mathcal {NRC}_{\lambda }( Set,Bag )$$ NRC λ ( S e t , B a g ) that combines set and multiset constructs. We show how to normalize and translate queries to SQL, and develop a novel approach to querying heterogeneous nested collections, based on the insight that “local” query subexpressions that calculate nested subcollections can be “lifted” to the top level analogously to lambda-lifting for local function definitions.

Question to Query: Converting Human Language to DBMS Query

In this paper a method has been proposed keeping in the mind the need for systems that could generate structured queries from normal language keeping in mind that the user has no prior knowledge of database query language. A novel method which aims at aiding analyst who aren’t well versed with codes, but need quantitative outputs to analyze, predict and alert the business or market. A python model is used, which aims at converting any sentence typed in English to a query provided that such tables and database is present for query processing. Tree tagging is used here to relate words typed in to SQL query syntax. Any sentence typed in by analyst, it further annotated by parts of speech and lemmas. A list of generic words and stop words is used while parsing the input the sentence and tagging it. Query is generated by simultaneously removing the stop words, mapping the keywords with the one’s used in structured query language. The generated query comes out in form of a JSON file.

Improving Data Management using Domain Knowledge

The development of tools and techniques for flexible and reliable data management is a long-standing challenge, ever more pressing in today’s data-rich world. We advocate using domain knowledge expressed in ontologies to tackle it, and summarize some research efforts to this aim that follow two directions. First, we consider the problem of ontology-mediated query answering (OMQA), where queries in a standard database query language are enriched with an ontology expressing background knowledge about the domain of interest, used to retrieve more complete answers when querying incomplete data. We discuss some of our contributions to OMQA, focusing on (i) expressive languages for OMQA, with emphasis on combining the open- and closed-world assumptions to reason about partially complete data; and (ii) OMQA algorithms based on rewriting techniques. The second direction we discuss proposes to use ontologies to manage evolving data. In particular, we use ontologies to model and reason about constraints on datasets, effects of operations that modify data, and the integrity of the data as it evolves.

The algebra of recursive graph transformation language UnCAL: complete axiomatisation and iteration categorical semantics

The aim of this paper is to provide mathematical foundations of a graph transformation language, called UnCAL, using categorical semantics of type theory and fixed points. About 20 years ago, Bunemanet al. developed a graph database query language UnQL on the top of a functional meta-language UnCAL for describing and manipulating graphs. Recently, the functional programming community has shown renewed interest in UnCAL, because it provides an efficient graph transformation language which is useful for various applications, such as bidirectional computation.In order to make UnCAL more flexible and fruitful for further extensions and applications, in this paper, we give a more conceptual understanding of UnCAL using categorical semantics. Our general interest of this paper is to clarify what is the algebra of UnCAL. Thus, we give an equational axiomatisation and categorical semantics of UnCAL, both of which are new. We show that the axiomatisation is complete for the original bisimulation semantics of UnCAL. Moreover, we provide a clean characterisation of the computation mechanism of UnCAL called ‘structural recursion on graphs’ using our categorical semantics. We show a concrete model of UnCAL given by the λG-calculus, which shows an interesting connection to lazy functional programming.