Pump Scheduling Research Articles

Interaction of natural and hydraulic fractures: the impact on reservoir pressure buildup and risk of shear fault reactivation in the Upper Devonian Duvernay Formation, Fox Creek, Alberta

The interaction of natural and hydraulic fractures may facilitate lateral fluid propagation in an unconventional reservoir resulting in fast fluid pressure transmission from treatment wells to a fault zone and potential fault shear slip reactivation and associated induced seismicity. Several induced earthquakes (up to 4.1 Mw) occurred since 2013 during hydraulic fracturing of the Upper Devonian Duvernay Formation in the Western Canada Sedimentary Basin. The mechanism of lateral fluid migration in the unconventional reservoir is not well understood. The current study aims to investigate the interaction of natural fractures and hydraulic fractures for the case study in the area south of Fox Creek, where a linear zone of induced earthquakes (up to 3.9 Mw) occurred along a fault in 2015 during hydraulic fracturing of horizontal wells. We analyze the growth of hydraulic fractures in presence of natural fractures, the impact of resulting complex fracture network on fluid transmission and fluid pressure buildup around the treatment wells. Hydraulic fracture modeling (HFM), reservoir simulations and 3D coupled reservoir-geomechanical modeling are applied to match the timing of hydraulic fracture propagation and transmitted fluid pressure increase in the fault zone versus induced earthquake occurrence. HFM results are verified by microseismic clouds distribution. Reservoir simulations are validated by a history matching of fluid injection volume and bottomhole pressure data. Additional HFM simulations are carried out to optimize the pumping schedule in the studied well pad that would help to prevent hydraulic fractures reaching the fault and minimize the risk of induced seismicity.Article highlightsStress anisotropy and simulated natural fractures impact lateral growth of complex hydraulic fractures and reservoir pressure buildup.Predicted fluid pressure transmission to a fault zone results in fault dextral shear slip reactivation matching induced seismicity.Optimized pumping schedule helps to minimize risks of fault reactivation and induced seismicity while preserving overall pad performance.

Real-Time Scheduling of Pumps in Water Distribution Systems Based on Exploration-Enhanced Deep Reinforcement Learning

Effective ways to optimise real-time pump scheduling to maximise energy efficiency are being sought to meet the challenges in the energy market. However, the considerable number of evaluations of popular optimisation methods based on metaheuristics cause significant delays for real-time pump scheduling, and the simplification of traditional deterministic methods may introduce bias towards the optimal solutions. To address these limitations, an exploration-enhanced deep reinforcement learning (DRL) framework is proposed to address real-time pump scheduling problems in water distribution systems. The experimental results indicate that E-PPO can learn suboptimal scheduling policies for various demand distributions and can control the application time to 0.42 s by transferring the online computation-intensive optimisation task offline. Furthermore, a form of penalty of the tank level was found that can reduce energy costs by up to 11.14% without sacrificing the water level in the long term. Following the DRL framework, the proposed method makes it possible to schedule pumps in a more agile way as a timely response to changing water demand while still controlling the energy cost and level of tanks.

A Novel Approach to Avoiding Technically Unfeasible Solutions in the Pump Scheduling Problem

Optimizing pump operation in water networks can effectively reduce the cost of energy. To this end, the literature provides many methodologies, generally based on an optimization problem, that provide the optimal operation of the pumps. However, a persistent shortcoming in the literature is the lack of further analysis to assess if the obtained solutions are feasible from the technical point of view. This paper first showed that some of these available methodologies identify solutions that are technically unfeasible because they induce tank overflow or continuous pump switching, and consequently, proposed a novel approach to avoiding such unfeasible solutions. This consisted in comparing the number of time-steps performed by the hydraulic simulator with the predicted value, calculated as the ratio between the simulation duration and the hydraulic time-step. Finally, we developed a new model which couples Epanet 2.0 with Pikaia Genetic Algorithm using the energy cost as an objective function. The proposed method, being easily exportable into existing methodologies to overcome the limitations thereof, thus represents a substantial contribution to the field of pump scheduling for optimal operation of water distribution networks. The new method, tested on two case studies in the literature, proved its reliability in both cases, returning technically feasible solutions.

Optimal Pump Control for Water Distribution Networks via Data-Based Distributional Robustness

In this article, we propose a data-based methodology to solve a multiperiod stochastic optimal water flow (OWF) problem for water distribution networks (WDNs). The framework explicitly considers the pump schedule and water network head level with limited information of demand forecast errors for an extended period simulation. The objective is to determine the optimal feedback decisions of network-connected components, such as nominal pump schedules and tank head levels and reserve policies, which specify device reactions to forecast errors for accommodation of fluctuating water demand. Instead of assuming that the uncertainties across the water network are generated by a prescribed certain distribution, we consider ambiguity sets of distributions centered at an empirical distribution, which is based directly on a finite training dataset. We use a distance-based ambiguity set with the Wasserstein metric to quantify the distance between the real unknown data-generating distribution and the empirical distribution. This allows our multiperiod OWF framework to trade off system performance and inherent sampling errors in the training dataset. Case studies on a three-tank WDN systematically illustrate the tradeoff between pump operational cost, risks of constraint violation, and out-of-sample performance.

Investigation the monthly effect of the pumping schedule on the groundwater resources in Al-Najaf City, Iraq

Investigation the monthly effect of the pumping schedule on the groundwater resources in Al-Najaf City, Iraq

Applications of Advanced Process Control Techniques to an Italian Water Distribution Network

In this paper an application of Advanced Process Control techniques to an Italian water distribution network is presented. Several in-depth hydraulic studies had previously been conducted in order to perform hardware modifications through sectorization procedures. In order to further improve performance, both pressure management of the water distribution system and optimization of pump scheduling problems have been addressed. Net pressure has been minimized through two-layer Model Predictive Control techniques, while advanced logics have been designed for the pumps scheduling. The developed Advanced Process Control system has been successfully installed allowing to reduce water losses and to achieve a significant reduction of operational costs in terms of electric energy and fault rates.

A computationally efficient model for fracture propagation and fluid flow in naturally fractured reservoirs

There is a need to develop computationally efficient models for oil and gas production from naturally fractured reservoirs. In this paper, we present an efficient, integrated fracturing-production simulator by combining a boundary element method (for fracture propagation) and a general Green's function solution (for fluid flow) that eliminates the need to discretize the matrix domain. First, the model is validated against analytical solutions and then compared with a fully numerical model. A comparison of results and computation time shows that our simulator significantly reduces the computation cost without any significant loss in accuracy. The simulator is then applied to investigate the effect of cluster spacing and pumping schedule on production from a hydraulically fractured horizontal well in a naturally fractured reservoir. The results show an optimal cluster spacing that can maximize the contact area between fractures and reservoirs while maintaining the highest production rate. Based on the chosen optimal cluster spacing, we observe that changes in the pumping schedule can have an impact on the production rate due to changes in the created fracture network.

Implementation and Field Test of Optimal Pump Scheduling in the Multiproduct Refined Oil Transmission System

Optimal pump scheduling ensures the safe and economic operation of the oil pipelines, which is also of vital significance for energy conservation as well as carbon emission reduction. This article proposes a framework adapting the data-driven pressure loss estimation with the long short-term memory neural network (LSTM-NN) and pump characteristics fitting with quadratic polynomials to model-based optimal pump scheduling for the multiproduct refined oil transmission system. The data-driven methods are data-adaptive with periodical field data in a rolling-training manner, and thus can reflect actual working conditions in the transmission pipelines. The presented LSTM-NN for pressure loss estimation integrating the characteristics of multiproduct refined oil pipeline transmission has good performance with an acceptable mean squared error of 0.016 MPa <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> in real applications. The optimization model makes the time precision down to the minute level, which is more convenient for the system dispatchers to operate the pumps following the optimal schedule. Practical implementation and field test are carried out in a real-world pipeline system, which verifies that the proposed framework is more practical and better-performed than the manual schedule.

Optimal pump scheduling in multi-phase distribution networks using Benders decomposition

With the growing adoption of renewable energy resources in the power grid, co-optimizing the operation of water and power distribution networks (WDNs and PDNs) increases the flexibility of both systems. The optimal water power flow problem (OWPF) is formulated to manage resource utilization across the WDNs and multi-phase PDNs. The nonlinear WDN hydraulic constraints and PDN ac power flow equations render the OWPF problem nonconvex. This paper formulates a convex optimal water power flow (C-OWPF) problem adopting successive approximations in WDNs and branch-flow relaxations in PDNs. The C-OWPF problem is a mixed-integer semidefinite program, which is computationally challenging even for small instances. Additionally, privacy considerations of WDN and PDN operators motivate the need for solving the C-OWPF in a distributed fashion. This paper develops a C-OWPF solver based on Benders decomposition that overcomes computational complexity challenges and preserve the privacy of the respective operators. The merits of the Benders decomposition-based solver are demonstrated on the IEEE 4-bus PDN coupled with a 3-node WDN and the IEEE 123-bus PDN coupled with the 36-node WDN.

Influence of sequential stimulation practices on geochemical alteration of shale

Water-based hydraulic fracturing fluids (HFFs) can chemically interact with formation shale, resulting in altered porosity and permeability of the host rock. Experimental investigations of spatial and temporal shale-HFF interactions are helpful in interpreting chemical compositions of the injectate, as well as predicting alteration of hydraulic properties in the reservoir due to mineral dissolution and precipitation. Most bench-top experiments designed to study shale-HFF chemical interactions, either using batch reactors or flow-through setups, are carried out assuming that the acid spearhead has already become mixed with neutral HFFs. During operations, however, HFFs are typically injected according to a sequenced pumping schedule, starting with a concentrated acid spearhead, followed by multiple additions of near-neutral pH HFFs containing chemical amendments and proppant. In this study, we use geochemical modeling to consider whether this pre-mixed experimental protocol provides results directly comparable to a sequential discrete fluid-shale interaction protocol. Our results show that for the batch system, the transient evolution in major ion concentrations is faster with the sequential procedure. After 2 h of reaction time, the two protocols converge to the same aqueous concentrations. In a flow-through geometry, the pre-mixed model predicts extensive chemical alteration close to the injection point but negligible alteration downstream. In contrast, the sequential model predicts mineral reactions over hundreds of meters along the flow path. The extent of shale alteration in the sequential model at a given location depends on shale mineralogy and where the acid spearhead resides during the shut-in period. The predictive model developed in this study can help experimentalists to design bench-top tests and operators to better translate the results of laboratory experiments into practical applications.

Pumping schedule assignment using chromatic graphs to reduce groundwater pumping energy consumption

Abstract In this paper, groundwater pumping schedules are examined, in terms of pumping energy consumption minimization. Chromatic graphs are used for the first time to assign groundwater pumping schedules. Assuming that a finite number of users pumps from a common aquifer, we examine the following three pumping scenarios: (a) all users pump simultaneously, (b) users pump during one out of two different periods, (c) users pump during one out four different periods. A computational code, based on Theis equation, is constructed to calculate the drawdowns and, subsequently, the energy consumption, for various well locations, numbers of wells, flow rates, pumping period durations and aquifers' characteristics. We use nearest neighbor graphs and chromatic graphs to represent the interactions of the well users. We compare the results obtained when chromatic graphs are used, to the optimum, which is obtained using genetic algorithms (GAs). Our results indicate that: (a) performance of chromatic graphs is good, (b) when two pumping periods are used, it is sufficient for each player to cooperate with their nearest neighbor and pump alternately. In typical cases, the energy consumption reduction can be around 10–40%. The benefit is even higher if four pumping periods are used.

A Chaotic Sobol Sequence-based multi-objective evolutionary algorithm for optimal design and expansion of water networks

The design of a water distribution network (WDN) is an optimization problem that is computationally challenging with conflicting objectives. This study offers an enhanced Chaotic Sobol Sequence-based Multi-Objective Self-Adaptive Differential Evolution (CS-MOSADE) algorithm for multi-objective WDN design. The CS-MOSADE algorithm was tested on two benchmark WDNs, and a real WDN. Optimization results indicate that the CS-MOSADE algorithm converged two to three times faster than the MOSADE and NSGA-IIalgorithms and led to better output in terms of even distribution of solutions and convergence towards the true Pareto-optimal front. Smaller spacing metric indicated better uniformity in the obtained solutions; and larger hyper-area and coverage function values depicted better convergence towards the true Pareto-optimal front for the CS-MOSADE algorithm compared to the other algorithms. The CS-MOSADE algorithm was then applied to solve a WDN expansion problem for optimal pump scheduling and minimization of Life Cycle Cost, maximization of reliability and minimization of Green House Gas (GHG) emissions. A significant reduction in GHG emissions of 2.17 x 106 kg was achieved at an additional cost of $0.55 x 107 when optimal pump scheduling was incorporated in the model of the real WDN over service life of 50 years.

Experimental investigation of proppant transport in hydraulically fractured wells using supercritical CO2

Supercritical CO2 fracturing is a promising waterless stimulation technology, and pumping proppant during fracturing is an essential part of the frac job design to obtain a high-conductivity flow channel after the artificial fracture is created. To date, only a few laboratory experiments have been conducted using supercritical CO2 directly to carry the proppant. In this study, an experimental system was designed to allow investigation of supercritical CO2 transport of proppants within a fracture. To determine the supercritical CO2 transport proppant mechanism and the impact of key operational variables, a series of laboratory experiments were performed considering various pumping schemes. The results demonstrate that the mechanism of supercritical CO2 transport proppant includes various flow patterns such as scouring, throwing and fluidization, among which fluidization is the key to the further migration of proppant. Continued injection of clear-fluid after the addition of proppant is complete, will help the dunes extend deeper into the fracture. Fluid velocity has a significant effect on supercritical CO2 transport proppant. While under the premise of constant mass flow rate, increasing the injection temperature, decreasing the injection pressure or reducing the proppant concentration will make the dune equilibrium height lower and the dune laying distance longer. Meanwhile, small-sized proppant particles can still be easily lifted and suspended in supercritical CO2, and reducing proppant size significantly increases the transportability. Furthermore, dune equilibrium height correlates significantly with the variation of the Reynolds number of the fluid flow. Therefore, from a practical field operation point of view, using Reynolds number as a controlling parameter seems to be a viable option for selection of optimum pumping operational variables and prediction of proppant migration within the fracture. The results of this study are expected to provide guidance for field operators to better design operational variables (e.g. CO2 injection rates, proppant load and pumping schedule, etc.) used for supercritical CO2 fracturing.

A real-time and eco-layout platform for optimization of supply/costs for water distribution systems management

Abstract This research discusses developing a coupled system with both structural and operational aspects of the water distribution system. Operating view is provided through a real-time control scheduler (RTC), which satisfactorily attends to solve the dynamic control problem at every timestep via minimization of energy costs over the day and satisfying hydraulic reliability constraints through suggesting near-optimal pump schedules. Moreover, the network layout provides the structural view with a focus on inventing the most prosperous cost-effective topology. The modeling process was exerted by group-based machine learning techniques, a dynamic random search (DDS) algorithm and ultimately, the model performance was evaluated. In addition to providing stable hydraulic conditions, evaluating the coupled optimization platform (RTC-eco-layout) formulation on a typical day was adequate to beget an average of up to 23% daily energy savings as well as preserving the hydraulic feasibility properties in the range of up to 30% energy cost economies. The sensitivity analysis admitted that both operating and environmental variables contribute to the total uncertainty.

ML-based energy management of water pumping systems for the application of peak shaving in small-scale islands

This study introduces an energy management method that smooths electricity consumption and shaves peaks by scheduling the operating hours of water pumping stations in a smart fashion. Machine learning models are first used to accurately forecast the electricity consumed and produced by renewable energy sources on an hourly level. Then, the forecasts are exploited by an algorithm that optimally allocates the operating hours of the pumps with the objective to minimize predicted peaks. Constraints related with the operation of the pumps are also considered. The performance of the proposed method is evaluated considering the case of a Greek remote island, Tilos. The island involves an energy management system that facilitates the monitoring and control of local water pumping stations that support residential water supply and irrigation. Results indicate that smart scheduling of water pumps in a small-scale island environment can reduce the daily and weekly deviation of electricity consumption by more than 15% at no monetary cost. It is also concluded that the potential gains of the proposed approach are strongly connected with the amount of load that can be shifted each day, the accuracy of the forecasts used, and the amount of electricity produced by renewable energy sources. • Electricity peak shaving in small scale islands. • Smart scheduling the operation of water pumping systems. • Exploiting ensembles of forecasting models for PV production and electricity consumption. • A heuristic optimization algorithm to support load shifting decisions.

Holistic Machine-Learning Approach Recognizes Trouble Stages

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 204145, “Employing a Suite of Machine-Learning Algorithms in a Holistic Approach to Trouble Stage Recognition and Failure Diagnostics,” by Jessica Iriarte, SPE, Well Data Labs; Matthew McConnell, SPE, Hawkwood Energy; and Sam Hoda, SPE, Well Data Labs, et al. The paper has not been peer reviewed. The complete paper explores a holistic approach to characterizing trouble stages by applying automated event recognition of abnormal pressure increases and associating those events with formation and operational causes. This analysis of pressure increases provides insight into the potential causes of operational difficulties, and the related diagnostics can suggest improvements to future pump schedules. Improving how stages are pumped is profitable in both the short and the long term. Quantifying how design decisions ultimately affect operations can help decrease the frequency of operational problems and help realize these gains. Introduction The Cretaceous Eagle Ford Shale is the source rock for the Woodbine, Buda, and Austin Chalk. The wells included in this study are between the San Marcos Arch and the eastern edge of the East Texas Basin (Brazos and Burleson Counties). The Eagle Ford is similar to many other resource plays, such as the Marcellus, Utica, Barnett, Bakken, Haynesville, and Woodford shales. Among the many characteristics that make these plays similar is that each formation has provided stages with abnormal pressure behaviors (APBs).

Development of optimal pump schedules for improved energy efficiency in water supply systems (case of NWSC)

The water utility in Uganda (NWSC) was under pressure from regulators, environmentalists and board members to reduce energy costs. However, achieving energy efficiency in the water distribution systems of Kampala that is characterized by variable demands and prescribed pressures would be impossible if this utility continued operating on trial and error methods. This research was undertaken in the year 2016-2017 and aimed at exploring how pump schedule optimization could enable NWSC to deal with the challenge of high energy costs and improve water utility performance. In this research, the energy consumption of existing pumps was obtained from historical data, data was diagnosed and based on the diagnostic findings, decision variables were selected and optimal pump schedules were formulated. The formulated schedules were applied to the Gabba Muyenga supply system of National Water and Sewerage Company (Uganda) as a proof of concept. The formulated pump schedules when applied on different pumps classified as models 1, 2 and 3 based on pump flow ratings and motor voltage ratings, results show that scheduling pump operations based on time of the day tariffs enabled NWSC to save about 0.373 Million kWh annually. On the other hand pump scheduling based on pressure, modulation had the potential to reduce water losses enabling NWSC to save 12 m3/hr equivalent to 0.068 million kWh per year in energy terms without compromising customer service levels and this was only for the established DMA within the case study area and not for the entire NWSC water distribution network. The data presented were obtained through field measurements, statistical analysis and hydraulic design calculations

The influence of heterogeneous proppant pack on fracture closure and productivity

Even though hydraulic fracturing is a common and well adopted well stimulation method in petroleum industry, there are still open scientific questions in the area, mainly due to complexity of the phenomena involved in the process. In this paper, we particularly address the problem of fracture closure and productivity in situations, when proppant distribution inside the fracture is strongly heterogeneous. This can happen, for instance, during pulsed injection, in which a pulse of clean fluid is alternated with a pulse of slurry with proppant. Such a combination leads to Saffman–Taylor instability and, consequently, to spatially heterogeneous distribution of proppant. To address the problem, a coupled hydraulic fracturing and proppant transport model is utilized to simulate proppant placement in a fracture. After that, a three-dimensional finite element model is used to compute fracture closure and fluid inflow or productivity. Several numerical examples with various pumping schedules are presented to better understand the influence of proppant heterogeneity on fracture closure and the resulting production. In addition, to get insights about the observed behavior, an analytical model for fracture closure between two proppant pillars is presented. The model provides a single dimensionless parameter that quantifies the degree of fracture closure between the pillars of proppant. Numerical results are analyzed and discussed in relation to this dimensionless number.

Effect of proppant pumping schedule on the proppant placement for supercritical CO2 fracturing

Supercritical CO2 fracturing is a potential waterless fracturing technique which shows great merits in eliminating reservoir damage, improving shale gas recovery and storing CO2 underground. Deep insight into the proppant-transport behavior of CO2 is required to better apply this technique in the engineering field. In the present paper, we adopted a coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) approach to simulate the proppant transport in a fracking fracture with multiple perforation tunnels. Previous experiments were first simulated to benchmark the CFD-EDM approach, and then various pumping schedules and injection parameters (injection location, multi-concentration injection order, multi-density injection order and injection temperature) were investigated to determine the placement characteristics of proppant. Results indicate that the swirling vortex below the injection tunnels dominates the proppant diffusion in the fracture. The velocity of fluid flow across the proppant bank surface in multi-concentration injection shows a positive correlation with the proppant concentration. Injecting high-density proppant first can promote the transportation of low-density proppant injected later in the fracture to a certain extent. Decreasing the initial injection temperature of supercritical CO2 slurry helps enhance the particle-driving effect of fluid and improve the performance of supercritical CO2 in carrying proppant.

Severe Casing Failure in Multistage Hydraulic Fracturing Using Dual-Scale Modeling Approach

Summary Field evidence of production logs after fracturing have documented the existence of abundant natural fractures in Weiyuan shale plays, which is widely acknowledged to have a positive impact on fracture network complexity. On the contrary, cases of severe casing failures have been frequently reported in this field during multistage fracturing practice. Stress interference between two adjacent stages may increase nonuniform loading on the casing string and accommodate failure. To better understand this problem, we establish a coupled 3D reservoir-scale model with complex well trajectory and tie it to a single well-scale model consisting of casing and the surrounding cement sheath. Using this model, we investigate the potential impacts of cement deficiency, clustered perforations, fracture geometry, and spacing strategy on casing integrity. Our simulation results indicate that cement deficiency could intensify the load nonuniformity around the borehole, which poses potential threats to casing failure. When cement deficiency reaches 45° along the minimum horizontal stress, it has the largest influence on the stress level of casing. In addition, perforations could lower the casing strength, but the reduction may not change further when the perforation diameter reaches a certain value. Also, impacts of fracture geometry and spacing on casing deformation are explored. We can conclude that a lower ratio of fracture length to width and reasonable spacing strategy could help reduce the load nonuniformity on casing and thus avoid the casing deformation. The described workflow may be adopted in other areas to predict the possible casing failure problems induced by multistage hydraulic fracturing with cheap computational costs and to anticipate the challenges and address them by revising pumping schedule or spacing strategy.