A novel approach in extracting predictive information from water-oil ratio for enhanced water production mechanism diagnosis

Despite the advances in water shutoff technologies, the lack of an efficient diagnostic technique to identify excess water production mechanisms in oil wells is preventing these technologies being applied to deliver the desired results, which costs oil companies a lot of time and money. This paper presents a novel integrated approach for diagnosing water production mechanisms by extracting hidden predictive information from water-oil ratio (WOR)graphs and integrating it with static reservoir parameters. Two common types of excess water production mechanism(coning and channelling) were simulated where a wide range of cases were generated by varying a number of reservoir parameters. Plots of WOR against oil recovery factor were used to extract the key features of the WOR data. Tree-based ensemble classifiers were then applied to integrate these features with the reservoir parameters and build classification models for predicting the water production mechanism. Our results show high rates of prediction accuracy for the range of WOR variables and reservoir parameters explored, which demonstrate the efficiency of the proposed ensemble classifiers. Proactive water control procedures based on proper diagnosis obtained by the proposed technique would greatly optimise oil productivity and reduce the environmental impacts of the unwanted water

Use of Local Plants for Ecological Restoration and Slope Stability: A Possible Application in Yan\u27an, Loess Plateau, China

This paper aimed to screen the potential species suitable for ecological restoration and slope stability from local natural growing plants in China Loess Plateau under a semiarid climate. As part of the field investigations of local natural growing plants, potential species, which are suitable candidates for ecological restoration and slope stability, were nominated in the hilly-gullied region in the Yan’an area. The results showed that Artemisia spp. is the best candidate to form a stable root-soil composite system to support the loose loess and reinforce the loose soil, particularly suitable as pioneer plant in the initial stage of loess slope ecosystem reconstruction. Field root pull-out test and direct shear test for soil without roots and root-soil composite systems were conducted to analyse the reinforcement effect of Artemisia spp. The results from quantitative analysis of the slope protection effect showed that the slope safety factor could be obviously improved by the growth of Artemisia spp. As the survey, test, stability analysis and case study shown, Artemisia spp. can effectively prevent the occurrence of loess flow slides and shallow landslides, which has extensive application prospect

Primary recovery factor as a function of production rate: implications for conventional reservoirs with different drive mechanisms

This study evaluates the dependency of production rate on the recovery of hydrocarbon from conventional reservoirs using MBAL simulator. The results indicated that the recoveries are sensitive to the production rate in almost all hydrocarbon reservoirs. It was also found that the recovery of volumetric gas drive reservoirs is not impacted by the production rate. In fact, any increase in the production rate improves gas recovery in weak and strong water drive reservoirs. Moreover, increasing the production rate in oil reservoirs decreases the recovery with a significant effect observed in the weak water drive reservoirs. The results of this study demonstrate the need for implementing an effective reservoir management in order to obtain a maximum recovery

A Review of Knowledge Graph Completion

Information extraction methods proved to be effective at triple extraction from structured or unstructured data. The organization of such triples in the form of (head entity, relation, tail entity) is called the construction of Knowledge Graphs (KGs). Most of the current knowledge graphs are incomplete. In order to use KGs in downstream tasks, it is desirable to predict missing links in KGs. Different approaches have been recently proposed for representation learning of KGs by embedding both entities and relations into a low-dimensional vector space aiming to predict unknown triples based on previously visited triples. According to how the triples will be treated independently or dependently, we divided the task of knowledge graph completion into conventional and graph neural network representation learning and we discuss them in more detail. In conventional approaches, each triple will be processed independently and in GNN-based approaches, triples also consider their local neighborhood

Modeling Temporal Dependence of Average Surface Treating Pressure in the Williston Basin Using Dynamic Multivariate Regression

The oil and gas industry has shifted paradigms after seeing the drastic decrease in oil prices since 2015. Companies are now focused as much on cost reduction as much as production maximization to drive profitable operations. This aspect is more prevalent in unconventional plays with the need for long horizontal drilling and hydraulic fracturing (HF) operations to develop and produce from the tight reservoirs. There exists an optimum point between the costs of HF treatment and the expected production. Because of the paradigm shift, many operators are now focused on re-developing existing assets at much lower costs instead of developing newer, more costly assets. Re-fracturing existing wells provides an opportunity for companies to add economical wells to their portfolio. Re-fracturing consists of pumping HF treatments in wells that were previously drilled and completed. Although it may seem that the HF process on a well would be easier the second time around, this is not always the case. There are often numerous operational and engineering parameters that may cause screen outs due to excessively high surface treating pressure (STP) that can drastically affect the economics of a re-fractured well. Being able to isolate the effects of these parameters and estimate their marginal effect on treatment will help engineers design to better HF treatments and surface equipment to effectively implement treatments in the field. This novel study uses field treatment data from re-fractured wells to create dynamic multivariate regression models to characterize the effects of treatment parameters on the average STP. The model allows for engineers to isolate the effects of other treatment parameters and estimate their marginal effects on average STP by holding other variables of interest constant. The model also attempts to account for the temporal dependence of stress shadow effects from the previous zones by using the average STP as a good approximation. It was found that the distance between zones (perforation standoff) was statistically significant at the 90% level, average pump rate, acid volume displaced, and the presence of a 3.5” liner were all statistically significant predictors of average STP at the 95% level and average surface treating pressure from the previous stage at 99% significance. The model was used to predict the STP for another re-fractured well, which showed reasonable results

Field Measurement and Research on Environmental Vibration due to Subway Systems: A Case Study in Eastern China

With the rapid development of subway systems, the negative environmental impacts of vibration induced by subways has gradually become a research hotspot. For the purpose of developing predictive models of vibration and designing effective vibration mitigation systems, continuous field dynamic measurements were conducted simultaneously in a subway tunnel, ground, and building in eastern China, the most prosperous region in China. The characteristics of vibration transmission and attenuation induced by subway were analyzed by statistical analysis of large amounts of measurement data. The results showed that most prominent and visible attenuation of vibration is from the track to the ballast bed in the tunnel, where the ground-borne vibration would quickly decrease exponentially with distance. The results also showed that the measured attenuation value of indoor vibration was approximate 0.76 dB on average between each floor. Moreover, the decay ratio of the vibration increased with the increase in the frequency range. Based on these findings, construction gauge of 20–25 m outside of the tunnel is recommended. In addition, reducing the vibration source excitation intensity is the most effective vibration isolation method, especially by track structural transformation