Integrated artificial neural network and object-based modelling for enhancement history matching in a fluvial channel sandstone reservoir

Hung Vo Thanh, Yuichi Sugai, Ronald Nguele, Kyuro Sasaki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Modelling accurately lithofacies and petrophysical properties is an important yet challenging process especially at the beginning of exploration and production from hydrocarbon reservoirs. However, the limited amount of well data and core data are the main issues facing conventional modelling processes. In this paper, Artificial Neural Network (ANN), Sequential Gaussian Simulation (SGS), Co-kriging and object-based modelling (OBM) were integrated as the enhancement framework for lithofacies and petrophysical properties modelling in the fluvial channel sandstone reservoir. In the OBM, multiple fluvial channels were generated in the lithofacies model. The result of this model represented all the characteristic of the fluvial channel reservoir. The model was then distributed with channels, crevasse, and leeves depositional facies with background shale. Multiple geological realizations were made and cross-validation to select the most suitable lithofacies distribution. This model was cross-validated by modelling the porosity and permeability properties using Sequential Gaussian Simulation. Thereafter, the modelling process continued with Artificial Neural Network. Petrophysical properties (mainly porosity and permeability) were predicted by training various seismic attributes and well log data using the ANN. Applying the co-kriging algorithm, the predicted ANN model was integrated with OBM simulated lithofacies model to preserve the fluvial features of the geological system. To achieve full field history matching, the final geological model was upscaled to serve as input data in dynamic history matching. An excellent and nearly perfect history matching with a least mismatch was obtained between the measurement and simulated bottom hole pressure from well test and production history. The results indicated that an efficient integrated workflow of ANN and other geostatistical approaches are imperative to attaining an excellent history matching.

Original languageEnglish
Title of host publicationSociety of Petroleum Engineers - SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition 2019, APOG 2019
PublisherSociety of Petroleum Engineers
ISBN (Electronic)9781613996478
Publication statusPublished - Jan 1 2019
EventSPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition 2019, APOG 2019 - Bali, Indonesia
Duration: Oct 29 2019Oct 31 2019

Publication series

NameSociety of Petroleum Engineers - SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition 2019, APOG 2019

Conference

ConferenceSPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition 2019, APOG 2019
CountryIndonesia
CityBali
Period10/29/1910/31/19

All Science Journal Classification (ASJC) codes

  • Fuel Technology
  • Energy Engineering and Power Technology

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  • Cite this

    Thanh, H. V., Sugai, Y., Nguele, R., & Sasaki, K. (2019). Integrated artificial neural network and object-based modelling for enhancement history matching in a fluvial channel sandstone reservoir. In Society of Petroleum Engineers - SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition 2019, APOG 2019 (Society of Petroleum Engineers - SPE/IATMI Asia Pacific Oil and Gas Conference and Exhibition 2019, APOG 2019). Society of Petroleum Engineers.