Scale-Prediction/Inhibition Design Using Machine-Learning Techniques and Probabilistic Approach

Abstract

Summary This paper presents a data-driven methodology to predict calcium carbonate (CaCO3)-scale formation and design its inhibition program in petroleum wells. The proposed methodology integrates and adds to the existing principles of production surveillance, chemistry, machine learning (ML), and probability theory in a comprehensive decision workflow to achieve its purpose. The proposed model was applied on a large and representative field sample to verify its results. The method starts by collecting data such as ionic composition, pH, sample-collection/inspection dates, and scale-formation event. Then, collected data are classified or grouped according to production conditions. Calculation of chemical-scale indices is then made using techniques such as water-saturation level, Langelier saturation index (LSI), Ryznar saturation index (RSI), and Puckorius scaling index (PSI). The ML part of the method starts by dividing the data into training and test sets (80 and 20%, respectively). Classification models such as support-vector machine (SVM), K-nearest neighbors (KNN), gradient boosting, gradient-boosting classifier, and decision-tree classifier are all applied on collected data. Prediction results are then classified into a confusion matrix to be used as inputs for the probabilistic inhibition-design model. Finally, a functional-network (FN) tool is used to predict the formation of scale. The scale-inhibition program design uses a probabilistic model that quantifies the uncertainty associated with each ML method. The scale-prediction capability compared with actual inspection is presented into probability equations that are used in the cost model. The expected financial impact associated with applying any of the ML methods is obtained from defining costs for scale removal and scale inhibition. These costs are factored into the probability equations in a manner that presents incurred costs and saved or avoided expenses expected from field application of any given ML model. The forecasted cost model is built on a base-case method (i.e., current situation) to be used as a benchmark and foundation for the new scale-inhibition program. As will be presented in the paper, the results of applying the preceding techniques resulted in a scale-prediction accuracy of 95% and realized threefold cost-savings figures compared with existing programs.

A systematic review of data science and machine learning applications to the oil and gas industry

“…scenarios using digital siblings, which are copies of the physical asset. Al-Hajri et al (2020) suggested a coupled machine learning and probabilistic models to predict the scale prediction and plan for inhibition. The coupled model was able to predict the scale and quantify the data-driven models' goodness, then quantify the savings.…”

A Data Mining Perspective on the Confluent Ions` Effect for Target Functionality

“…The data used in the present work was obtained from the open literature as well as our own experiments [4,25,[27][28][29][30]. As explained earlier, the data was collected so as to target three main functionalities minimizing permeability impairment (I), minimizing the possibility of scale damage in the field (II), maximizing oil recovery from matrix (III).…”

“…The nature of the data related to this functionality had been of the categorical type (i.e. TRUE/FALSE), which referred to the occurrence of calcium carbonate scale formation in the field [4]. The data was also more limited than the data in the other two functionalities, in terms of the number of macro-scale parameters enlisted -mostly considering ions data for a practical purpose.…”

“…Given its importance, several studies have focused on understanding the effects of different parameters on the scale formation phenomenon and proposing relevant mechanisms. According to the literature, the scale formation at oilfield is linked with operational parameters such as field type as well as laboratory parameters such as the concentration of selected ions with the latter being tested both inside inhibitor-free and inhibitor-containing environments [3][4][5][6]. Nevertheless, the theories evolved over the deposition mechanism are non-overlapping and there exists a great deal of ambiguity about the influencing rank of the found parameters in the sequel -a question which this work attempts to address.…”

“…These attempts have covered aspects of enhanced oil recovery [7][8][9][10][11][12][13][14], fracture detection [15], development plan optimization [15,16], dynamic production prediction [18][19][20][21] and asphaltene precipitation prediction [22]. Some studies have also focused on applying machine learning strategies to model permeability impairment due to mineral scale deposition [23][24][25] and predict the success of an inhibition scenario in the field [4]. The bulk of these works have adopted an Artificial Neural Network (ANN) technique for their analysis [26], albeit some hybrid strategies have also been tested.…”