Sequence Mining and Pattern Analysis in Drilling Reports with Deep Natural Language Processing

Drilling activities in the oil and gas industry have been reported over decades for thousands of wells on a daily basis, yet the analysis of this text at large-scale for information retrieval, sequence mining, and pattern analysis is very challenging. Drilling reports contain interpretations written by drillers from noting measurements in downhole sensors and surface equipment, and can be used for operation optimization and accident mitigation. In this initial work, a methodology is proposed for automatic classification of sentences written in drilling reports into three relevant labels (EVENT, SYMPTOM and ACTION) for hundreds of wells in an actual field. Some of the main challenges in the text corpus were overcome, which include the high frequency of technical symbols, mistyping/abbreviation of technical terms, and the presence of incomplete sentences in the drilling reports. We obtain state-of-the-art classification accuracy within this technical language and illustrate advanced queries enabled by the tool.Comment: 7 pages, 14 figures, technical repor

The inverse problem of history matching, a probabilistic framework for reservoir characterization and real time updating

Em Engenharia de Petróleo e outras áreas da ciência, Mitigação de Incertezas baseada em Histórico (MIH) é o termo moderno usado por especialistas ao se referirem a ajustes contínuos de um modelo matemático dadas observações. Tais ajustes tem maior valor quando acompanhados de diagnósticos que incluem intervalos de confiança, momentos estatísticos, e idealmente caracterização completa das distribuições de probabilidade associadas. Neste trabalho, o bastante conhecido problema de ajuste ao histórico em campos de petróleo é revisado sob uma perspectiva Bayesiana que leva em consideração toda possível fonte de incerteza teórica ou experimental. É uma aplicação direta da metodologia geral desenvolvida por Albert Tarantola no seu livro intitulado ‘’Inverse Problem Theory and Methods for Model Parameter Estimation”. Nosso objetivo é fornecer a pesquisadores da área de Óleo & Gás um software escrito em uma linguagem de programação moderna (i. e. Python) que possa ser facilmente modificado para outras aplicações; realizar a inversão probabilística com dezenas de milhares de células como uma prova de conceito; e desenvolver casos de estudo reproduzíveis para que outros interessados neste tema possam realizar “benchmarks” e sugerir melhoramentos. Diferentemente de outros métodos de sucesso para MIH como Ensemble Kalman Filters (EnKF), o método proposto, denomidado Ensemble MCMC (EnMCMC), não assume distribuições a priori Gaussianas. Pode ser entendido como uma cadeia de Markov de ensembles e teoricamente é capaz de lidar com qualquer distribuição de probabilidade multimodal. Dois casos de estudo sintéticos são implementados em um cluster de computação de alto desempenho usando o modelo MPI de execução paralela para distribuir as diversas simulações de reservatório em diferentes nós computacionais. Resultados mostram que a implementação falha em amostrar a distribuição a posteriori, mas que ainda pode ser utilizada na obtenção de estimativas maximum a posteriori (MAP) sem fortes hipóteses a respeito dos dados (e. g. a priori Gaussianas)

LAMPSPUC/StateSpaceModels.jl: v0.5.9

StateSpaceModels v0.5.9 Diff since v0.5.8 Closed issues: Add a small example on README.md (#189) Make an ETS that allows for all linear models (#233) Merged pull requests: Add ETS models (#254) (@guilhermebodin) Improve table print and add model name (#255) (@guilhermebodin) Fix ETS documentation (#256) (@guilhermebodin) Add auto_ets function (#257) (@guilhermebodin

LAMPSPUC/StateSpaceModels.jl: v0.5.10

StateSpaceModels v0.5.10 Diff since v0.5.9 Merged pull requests: separe unit_root function into assert_stationarity and assert_invertibility (#259) (@iagochavarry) bump version (#261) (@guilhermebodin

Probabilistic knowledge-based characterization of conceptual geological models

The construction of conceptual geological models is an essential task in petroleum exploration, especially during the early stages of investment, when evidence about the subsurface is limited. In this task, geoscientists recreate the most likely geological scenarios that led to potential accumulation of reserves in a target block, based on past experience, historical analogues, and interpreted “signatures” that were left in the data by physical processes. Due to cognitive constraints, this task has traditionally focused on the single most likely conceptual scenario, or at most, a reduced set of scenarios chosen a priori via ad-hoc methods, which often lead to improper block valuation and severe money losses. In this work, we propose a probabilistic framework for reasoning about conceptual geological scenarios that helps domain experts maintain multiple hypotheses throughout the exploration program. The framework is extensible and can be instantiated automatically from simple knowledge templates, a form of “knowledge standard” in the company. We show how the acquired knowledge can be leveraged for uncertainty mitigation using concepts from information theory, and assess the framework qualitatively in a real case study

alan-turing-institute/MLJ.jl: v0.19.4

MLJ v0.19.4 <p><a href="https://github.com/alan-turing-institute/MLJ.jl/compare/v0.19.3...v0.19.4">Diff since v0.19.3</a></p> <p><strong>Merged pull requests:</strong></p> <ul> <li>Updating MLJBase.jl dep to last version (#1037) (@pebeto)</li> </ul&gt

alan-turing-institute/MLJ.jl: v0.10.1

MLJ v0.10.1 <p><a href="https://github.com/alan-turing-institute/MLJ.jl/compare/v0.10.0...v0.10.1">Diff since v0.10.0</a></p> <p>(<strong>enhancement</strong>) Add serialization for machines. Serialization is model-specific, with a fallback implementation using JLSO. The user serializes with <code>MLJBase.save(path, mach)</code> and de-serializes with <code>machine(path)</code> (#138, #292)</p> <p><strong>Closed issues:</strong></p> <ul> <li>Adhere by Invenia's bluestyle (#434)</li> <li>Update list of scikitlearn models in readme table. (#469)</li> </ul> <p><strong>Merged pull requests:</strong></p> <ul> <li>updated list of ScikitLearn models in Readme (#472) (@OkonSamuel)</li> <li>For a 0.10.1 release (#473) (@ablaom)</li> </ul&gt

alan-turing-institute/MLJ.jl: v0.20.0

MLJ v0.20.0 Diff since v0.19.5 (breaking) Adapt to the migration of measures from MLJBase.jl to StatisticalMeasures.jl (#1054). See the MLJBase 1.0 migration guide for details. Merged pull requests: CI: fix the YAML syntax for the docs job, and thus properly surface any docbuild failures (#1046) (@DilumAluthge) Update docs (#1048) (@ablaom) Try again to generate the documentation (#1049) (@ablaom) docs/make.jl: set devbranch to master, which means that the docs will be deployed for pushes to `master (#1051) (@DilumAluthge) Try to deploy docs again x 3 (#1052) (@ablaom) Adapt to migration of measures MLJBase.jl -> StatisticalMeasures.jl (#1054) (@ablaom) For a 0.20 release (#1060) (@ablaom) Closed issues: Julia crashes when fitting a SVC (#1030) Update deprecated document example in "Transformers ..." section of manual (#1040) fit! not exported in 0.19.3/0.19.4? (#1041) Doc generation is failing silently (#1045

alan-turing-institute/MLJ.jl: v0.20.1

MLJ v0.20.1 Diff since v0.20.0 (new feature) Add the BalancedModel wrapper from MLJBalancing.jl (#1064) (docs) Add the over/undersampling models from Imbalance.jl to the Model Browser (#1064) Merged pull requests: Add MLJBalancing to MLJ and add class imbalance docs (#1064) (@ablaom) For a 0.20.1 release (#1065) (@ablaom) Closed issues: Oversampling and undersampling (#661) [Tracking] Migration of measures MLJBase.jl -> StatisticalMeasures.jl (#1053) Include MLJBalancing.jl in MLJ and re-export it's names. (#1062) Update docs for new class imbalance support (#1063

alan-turing-institute/MLJ.jl: v0.19.5

MLJ v0.19.5 <p><a href="https://github.com/alan-turing-institute/MLJ.jl/compare/v0.19.4...v0.19.5">Diff since v0.19.4</a></p> <ul> <li>Correct problem with previous version in which some methods were not exported, namely: <code>source</code>, <code>node</code>, <code>fit!</code>, <code>freeze!</code>, <code>thaw!</code>, <code>Node</code>, <code>sources</code>, <code>origins</code> (#1043) @pebeto</li> </ul> <p><strong>Closed issues:</strong></p> <ul> <li>Is the Averager documentation deprecated? (#1039)</li> </ul> <p><strong>Merged pull requests:</strong></p> <ul> <li>Adding necessary exports (#1043) (@pebeto)</li> <li>For a 0.19.5 release (#1044) (@ablaom)</li> </ul&gt