Summary Acid jetting occurs as a result of pumping acid through limited-entry liner completions, causing high-velocity streams to impinge against the wellbore wall. The dissolution effect of jetting differs significantly from conventional matrix acidizing. Acid jetting causes cavities to be formed at the points of contact of the jet with the rock, with wormholes forming beyond the cavity. Jetting has been shown to be an effective technique for placing acid along extended-reach laterals, removing filter cake, and enhancing wormhole propagation. The velocity of the impinging jet and its standoff distance from the rock cause some of the acid to penetrate the formation and some to flow back in the annular space of the liner. Two types of dissolution mechanisms occur: surface dissolution forming the cavity and matrix dissolution forming the wormholes. These dissolution mechanisms are highly dependent on the acid-injection rate, velocity of the jet, temperature, and permeability of the formation. The differences between the matrix dissolution mechanism of acid jetting and that of conventional matrix acidizing are most obvious at low acid-injection rates. Experiments were performed with the intention of quantifying the difference in pore volume (PV) to breakthrough between acid jetting and matrix acidizing, as well as determining the effect of increased temperature, rock permeability, and acid concentration on this value with respect to the acid-injection rate. The baseline parameters of room temperature, 15% hydrochloric (HCl) acid, and 2- to 4-md Indiana limestone were individually compared with experiments run at 180°F, 28% HCl, and Indiana limestone cores of 30, 60, and 140 md. The effect of jetting velocity was also investigated. A direct comparison with conventional matrix acidizing was made by eliminating the jetting effect of the stream through mechanical dispersion. Acid jetting creates a point of heightened interstitial velocity at the contact of the acid and the rock, causing wormhole propagation to occur at a faster rate than it would in conventional matrix acidizing at that injection rate. This effect is especially pronounced as the jetting velocity is increased above that of matrix acidizing, and it tapers off at progressively higher jetting velocities.

The objective of this study is to present a novel rock formation identification model using a data-driven modeling approach. This study explores the use of real-time drilling data to train and validate a classification model to improve the efficiency of the drilling process by reducing Mechanical Specific Energy (MSE). In this study, we demonstrate the feasibility of a layer-based determination and change detection of properties of rock formation currently being drilled as accurately and fast as possible. Data for this study was collected from a custom-built lab-scale drilling rig equipped with multiple sensors. The experiment was conducted by drilling through an arrangement of different rock formations of varying rock strength properties. Data was recorded and stored at a frequency of 2 kHz, then filtered, processed, and downsampled to extract relevant features. This dataset was used to train an Artificial Neural Network and other machine learning classification algorithms. Feature selection was made first with ten most notable features found by Random Forest, and the second set with derived measurements and down-sampled dynamic features from the sensors. The classification analysis was divided into two steps: the best predictors/features extraction and classification model building. The models were trained using multiple classification algorithms, namely logistic regression, linear discriminant analysis (LDA), Support Vector Machines (SVM), Random Forest (RF), and Artificial Neural Networks (ANN). It was found that random forest and ANN performed the best with prediction accuracy of 99.48% and 99.58%, respectively, for the data set with ten most prominent features. The high prediction rate accuracy for the most prominent predictors suggests that if the high-frequency data can be processed in real-time, predicting what formation we are drilling in is possible to achieve in near real-time. This can lead to significant savings for drilling companies as optimal drilling parameters can be computed, and in turn, optimized Mechanical Specific Energy can be obtained in real-time. Since the rock formation identification is time-consuming, we also describe here an alternative approach using slightly less accurate but equally powerful dynamic predictors. In this case, we show that our dynamic predictor models with RF and ANN yielded prediction accuracy of 96.30% and 95.61%, respectively. Both the prominent feature and dynamic predictor approaches are described in detail in this paper. Our results suggest that accurately predicting rock formation type in real-time while drilling is very much feasible with lesser computational cost and complexity. This study provides the building blocks for the development of a completely autonomous downhole device and Electronic Device Recorders (EDR) that reduces the need for highly sophisticated sensors or data transmission processes downhole.

A database is developed from multiple sources to comprehensively present and evaluate enhanced oil recovery (EOR) projects in Canada. The datasets comprising of in-situ reserves, corresponding production and EOR information of Canada and worldwide EOR projects are made publicly available on a custom-built interactive data analysis platform TIBCO Spotfire. EOR projects for light, medium and heavy oils in Canada are classified into 65 solvent flooding, 6 polymer flooding and 13 Alkali-Surfactant-Polymer (ASP) flooding. Thermal methods, particularly SAGD & CSS are conducted for extra heavy oil and bitumen in oil sands. Another 31 of immiscible flooding projects are also identified. Factors contributing to success of these projects in respective fields are evaluated based on reservoir properties and EOR parameters such as miscibility, wettability, mobility ratio, capillary number, minimum miscibility pressure. With detailed technical analysis, EOR screening criterion for each method is updated and validated with world EOR data as well as Alberta oil pools data of 2019. Primary and secondary flooding projects that have potential for tertiary recovery are matched with historic EOR data to estimate future enhanced production. It has been observed that 257 pools currently employing water flooding have some similarities to fields that have seen success with EOR. Upgrading these primary or secondary projects to enhanced recovery has the potential of increasing daily production by as much as 14%.

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