A New Bayesian Approach for Analogs Evaluation in Advanced EOR Screening

Abstract

We present and test a new screening methodology to discriminate amongst alternative and competing Enhanced Oil Recovery (EOR) techniques to be considered for a given reservoir. Our work is motivated by the observation that, even if a considerable variety of EOR techniques have been successfully applied to extend oilfield production and lifetime, an EOR project requires extensive laboratory and pilot tests prior to field-wide implementation and preliminary assessment of EOR potential in a reservoir is critical in the decision-making process. Since similar EOR techniques may be successful in fields sharing some global features, as basic discrimination criteria we consider fluid (density and viscosity) and reservoir formation (porosity, permeability, depth and temperature) properties. Our approach is observation-driven and grounded on an exhaustive data-base which we compile upon considering worldwide EOR field experiences. A preliminary reduction of the dimensionality of the parameter space over which EOR projects are classified is accomplished through Principal Component Analysis (PCA). A screening of target analogs is then obtained by classification of documented EOR projects through a Bayesian clustering algorithm. Considering the cluster which comprises the EOR field under evaluation, an inter-cluster refinement is then accomplished by ordering cluster components on the basis of a weighted Euclidean distance from the target field in the (multidimensional) parameter space. Distinctive features of our methodology are that (a) all screening analyses are performed on the database projected onto the space of principal components, and (b) the fraction of variance associated with each principal component is taken as weight of the Euclidean distance we determine. As a test bed, we apply our approach on three fields operated by eni. These include light, medium and heavy-oil reservoirs, where Gas, Chemical and Thermal EOR projects have been respectively proposed. Our results are (a) conducive to the compilation of a broad and extensively usable data-base of EOR settings and (b) consistent with the field observations related to the three tested and already planned/implemented EOR methodologies, thus demonstrating the effectiveness of our approach.

Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms

“…Taber et al [6] Creating a Taber table for filtering Al Adasani et al [7] Updating Taber's table Saleh, L et al [9] Application of Box Plots to Polymer Projects Aldhaheri, M et al [10] Designing guidelines on gel processing design parameters AEORS Alvarado et al [12] The earliest proposal for the application of machine learning in EOR screening. Siena et al [13,14] Establishing a Bayesian EOR selection model Zhang et al [15] Analyzing EOR projects using clustering algorithms Khazali et al [17] Using the fuzzy decision tree method to classify the EOR techniques Cheraghi et al [19] Developing neural networks model to predict the category of suitable EOR methods…”

“…This is the earlier application of machine learning in EOR screening. Siena et al used the principal component analysis method to reduce the dimension of parameters and established a Bayesian EOR selection model based on reservoir/fluid properties to obtain the target technology for further research to establish a hierarchical clustering model [13,14]. Zhang et al used a hierarchical clustering algorithm combined with principal component analysis to analyze global steamflooding EOR projects [15].…”

Neuro-Simulation Tool for Enhanced Oil Recovery Screening and Reservoir Performance Prediction

“…The aforementioned model was able to predict oil recovery factor within roughly 3% average absolute error. Siena, M., et al [16] developed and tested new screening method for identifying most suitable EOR approach by means of gathered database from chemical, thermal and gas/WAG § injection projects, applying Principle Component Analysis(PCA) algorithm for data mining and to assess analogy between data and targets, employed Bayesian clustering algorithm. Eghbali, S., et al [17] employed expert fuzzy logic system to screen four noted EOR methods including miscible CO2 and HC gas injection, polymer flooding and steam injection, then developed a screening program capable of evaluating suitable EOR techniques and finally, drew distinction between output results of mentioned system and Bayesian Belief Network(BBN) model.…”