Excess water production diagnosis in oil fields using ensemble classifiers

In hydrocarbon production, more often than not, oil is produced commingled with water. As long as the water production rate is below the economic level of water/oil ratio (WOR), no water shutoff treatment is needed. Problems arise when water production rate exceeds the WOR economic level, producing no or little oil with it. Oil and gas companies set aside a lot of resources for implementing strategies to effectively manage the production of the excessive water to minimize the environmental and economic impact of the produced water.However, due to lack of proper diagnostic techniques, the water shutoff technologies are not always proficiently applied. Most of the conventional techniques used for water diagnosis are only capable of identifying the existence of excess water and cannot pinpoint the exact type and cause of the water production. A common industrial practice is to monitor the trend of changes in WOR against time to identify two types of WPMs, namely coning and channelling. Although, in specific scenarios this approach may give reasonable results, it has been demonstrated that the WOR plots are not general and there are deficiencies in the current usage of these plots.Stepping away from traditional approach, we extracted predictive data points from plots of WOR against the oil recovery factor. We considered three different scenarios of pre-water production, post-water production with static reservoir characteristics and postwater without static reservoir characteristics for investigation. Next, we used tree-based ensemble classifiers to integrate the extracted data points with a range of basic reservoir characteristics and to unleash the predictive information hidden in the integrated data. Interpretability of the generated ensemble classifiers were improved by constructing a new dataset smeared from the original dataset, and generating a depictive tree for each ensemble using a combination of the new and original datasets. To generate the depictive tree we used a new class of tree classifiers called logistic model tree (LMT). LMT combines the linear logistic regression with the classification algorithm to overcome the disadvantages associated with either method.Our results show high prediction accuracy rates of at least 90%, 93% and 82% for the three considered scenarios and easy to implement workflow. Adoption of this methodology would lead to accurate and timely management of water production saving oil and gas companies considerable time and money

Current technologies and the applications of data analytics for crude oil leak detection in surface pipelines

Pipeline pressure monitoring has been the traditional and most popular leak detection approach, however, the delays with leak detection and localization coupled with the large number of false alarms led to the development of other sensor-based detection technologies. The Real Time Transient Model (RTTM) currently has the best performance metric, but it requires collection and analysis of large data volume which, in turn, has an impact in the detection speed. Several data mining (DM) methods have been used for leak detection algorithm development with each having its own advantages and shortcomings. Mathematical modelling is used for the generation of simulation data and this data is used to train the leak detection and localization models. Mathematical models and simulation software have also been shown to provide comparable results with experimental data with very high levels of accuracy. While the ANN and SVM require a large training dataset for development of accurate models, mathematical modelling has been shown to be able to generate the required datasets to justify the application of data analytics for the development of model-based leak detection systems for petroleum pipelines. This paper presents a review of key leak detection strategies for oil and gas pipelines, with a specific focus on crude oil applications, and presents the opportunities for the use of data analytics tools and mathematical modelling for the development of a robust real time leak detection and localization system for surface pipelines. Several case studies are also presented

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

A numerical study to assess the effect of heterogeneity on CO2 storage potential of saline aquifers

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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

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

Accounting for Fixed Effects in Re-Fracturing Using Dynamic Multivariate Regression

The oil and gas (O&G) industry is now as focused on minimizing costs and maximizing efficiency just as much as maximizing production. Operators are looking for new and cost-effective ways to add profitable assets to their portfolio. One such way is to re-fracture existing wells. There is evidence that these wells can be very productive in the Bakken. However, because of factors such as depletion and aging wellbore material, re-fracturing wells can be a difficult process to implement successfully and often have binding constraints on surface treating pressure (STP). This study attempts to quantify the effects that completion parameters have on re-fracturing treatment implementation by constructing dynamic fixed effects (FE) multivariate regression models. These models are not generally used in O&G and are more commonly used in economics and policy analysis. However, given that both economics and O&G deal with large amounts of uncertainty for each individual person and well, respectively, these models provide a much simpler approach to handle the uncertainty. These models also attempt to account for stress shadow effects from subsequent stages on treatment. The FE model has the advantage of treating a compilation of well treatment data as panel data and differencing out any unobservable fixed parameters. To the authors’ knowledge, this is the first study using dynamic FE models to estimate temporal stress shadow effects from one stage to the next. These models may then be thought of as estimating the boundary effects from stress shadows, which will affect treatment implementation. The novelty lies in estimating these effects, while accounting for fixed within-well variation, using simpler models than those usually found in industry. We stress that the simplicity of these models is a feature, not a bug. This study found that previous stage average STP, acid volume pumped, and perforation standoff were all statistically significant predictors of average STP with a strong temporal dependence of average STP on subsequent stages after accounting for fixed wellbore and geologic parameters. The models in this study also predict a positive marginal effect from acid volume average STP, which may seem counterintuitive, but is also backed by a previous study

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