Copyright 2019 held jointly by the Society of Petrophysicists and Well Log Analysts (SPWLA) and the submitting authors. ABSTRACT Today, many machine learning techniques are regularly employed in petrophysical modelling such as cluster analysis, neural networks, fuzzy logic, self-organising maps, genetic algorithm, principal component analysis etc. While each of these methods has its strengths and weaknesses, one of the challenges to most of the existing techniques is how to best handle the variety of dynamic ranges present in petrophysical input data. Mixing input data with logarithmic variation (such as resistivity) and linear variation (such as gamma ray) while effectively balancing the weight of each variable can be particularly difficult to manage. DTA is conceived based on extensive research conducted in the field of CFD (Computational Fluid Dynamics). This paper is focused on the application of DTA to petrophysics and its fundamental distinction from various other statistical methods adopted in the industry. Case studies are shown, predicting porosity and permeability for a variety of scenarios using the DTA method and other techniques. The results from the various methods are compared, and the robustness of DTA is illustrated. The example datasets are drawn from public databases within the Norwegian and Dutch sectors of the North Sea, and Western Australia, some of which have a rich set of input data including logs, core, and reservoir characterisation from which to build a model, while others have relatively sparse data available allowing for an analysis of the effectiveness of the method when both rich and poor training data are available. The paper concludes with recommendations on the best way to use DTA in real-time to predict porosity and permeability. INTRODUCTION The seismic shift in the data analytics landscape after the Macondo disaster has produced intensive focus on the accuracy and precision of prediction of pore pressure and petrophysical parameters.
In the United Kingdom Continental Shelf (UKCS), a significant heavy oil prize of 9 billion barrels has been previously identified, but not fully developed. In the shallow unconsolidated Eocene reservoirs of Quads3 and 9, just under 3 billion barrels lie in the discovered, but undeveloped fields, of Bentley and Bressay. Discovered in the 1970s, they remain undeveloped due to the various technology challenges associated with heavy oil offshore and the presence of a basal aquifer. The Eocene reservoirs represent significant challenges to recovery due to the unconsolidated nature of the hydrocarbon bearing layers. The traditional view has been that such a nature represents a risk to successful recovery due to sand mobility; reservoir and near wellbore compaction; wormhole formation; and injectivity issues.
We propose improving the ultimate oil recovery by a combination of aquifer water production and compaction drive. By interpreting public domain data from well logs, the range of geomechanical properties of Eocene sands have been determined. A novel approach to producing the heavy oil unconsolidated reservoirs of the UKCS is proposed by producing the aquifer via dedicated water producers situated close to the oil-water contact. The location was determined by sensitivity analysis of water producer location and production rates. By locating water producers at the OWC with a production rate of 20,000 bbls/day of fluids, the incremental recovery at the end of simulation is increased by 4.1% OOIP of the total modelrelative to the ‘no aquifer production’, casesuggesting a significant increase in recovery can be achieved by producing the aquifer. A rate of 30,000 bbld/day located at the OWC was found to increase incremental recovery by 5.8 %OOIP relative to the ‘no aquifer case’. In all cases, as the reservoir fluid pressure is reduced, oil recovery increases via compaction and reduced water influx into the oil leg. This reduced pressure leads to a higher tendency towards reservoir compaction which is expressed as a change in mean effective stress and porosity reduction.