Detection of Stylolite Zones in Reservoirs Using Machine Learning Methods
Stylolite is a special geopattern that can occur in both sedimentary rocks and deformed zones. Such rough surfaces could change porosity of the matrix and modify the permeability either in a positive way by producing microcracks, or a negative way by dissolving soluble minerals and deposit in the near vicinity, which sometimes could even form to act as horizontal permeability barriers. Multiple fields in Abu Dhabi such as Upper Zakum oil field have encountered big production losses from such barriers in the past. As a potential approach to supplement traditional methods, machine learning methods can be used to locate a stylolite zone in a reservoir and further determine if it is barrier.
An Integrated Framework for Production Data Analysis Using Machine Learning and Wavelets
The modeling framework introduced the Maximum Overlap Discrete Wavelet Transform
Multiresolution Analysis (MODWT-MRA) as a useful transform for decomposing production time series data. Moreover, the research proved that applying the MODWT-MRA is equivalent to decomposing a single well's data into a set of virtual wells that present simpler behavior when compared to the original flowrate and pressure readings). These virtual wells decomposition is then leveraged with the use of machine learning and deep learning models to capture the reservoir response.
Automated Analysis of DTS Data
The research is focused in applying Machine Learning to aid with the interpretation of DTS data. The goal is to automatically detect the presence of fractures from the data and give an estimation of flow from them. Having the capacity to automate some of the interpretation would allow for the use of fiber optic data to real time decision making.
Large Volume Data Processing for Permanent Downhole Gauges
Permanent Downhole Gauge (PDG for short) is a newly developed tool for well testing in the petroleum industry. In traditional well testing, pressure and flow rate transient data are collected for a short period which leads to a large uncertainty. Due to long time continuous data acquisition, a PDG may provide measurements for several years or longer. However, at the same time, this brings a new problem--a large volume of noise together with large volume of measurement.
Modeling reservoir temperature transients, and matching to permanent downhole gauge (PDG) data for reservoir parameter estimation
Over the last decade, permanent download gauges (PDGs) have been used to provide a continuous source of downhole data in the form of pressure, temperature and sometimes flow rate. The tools provide access to data acquired continuously over a large period of time and containing reservoir information at a much larger radius of investigation than conventional wireline testing.