BACKGROUND:Treatment of large bone defects and fracture healing complications (delayed and non-union) presents a substantial challenge for orthopaedic surgeons. Given that bone healing requires mechanical stability as well as a favourable biological microenvironment, orthobiologics such as Platelet-Rich Plasma (PRP) may have a significant clinical role to play.AIMS:To perform a systematic review of the available literature to assess the clinical effect of PRP, with or without other orthobiologics, on bone healing.METHOD:Two independent reviewers performed the literature search based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Clinical studies of any evidence, assessing effect of PRP with or without other orthobiologics on bone healing, were included. A qualitative analysis was carried out on the clinical and radiological outcomes reported.RESULT:27 articles with 1631 patients (mean age = 43.56, 57.1% male, mean follow-up = 17.27 months) were included in the qualitative. Of the 27 studies, 13 dealt with fracture complications (delayed or non-unions), 7 with acute fracture healing, 4 with tibial osteotomies and lengthening procedures and 3 with lumbar spine pathology. 18/27 studies showed a clinical benefit of PRP, 8/27 showed no significant effect, and 1/27 showed a worse outcome with PRP.CONCLUSION:Our review suggests PRP may play a clinical role in bone healing but further randomised controlled trials (RCTs) using standardised outcomes should be performed to establish its efficacy.

Brinkman's model is one of the common mathematical formulations used in modeling fluid flow in karst aquifers. The Brinkman's model is a hybrid formulation that allows the use of a single transport equation to model fluid flow in both the free-flow and porous regions of a karst aquifer, by transitioning between the Stokes equation and the Darcy's law. However, the Brinkman's model is computationally expensive, requiring the solution of four different equations to obtain the pressures and velocities in a three-dimensional aquifer. Darcy's model provides a cheaper alternative, because it allows the substitution of the Darcy's equation into the mass conservation equation and thus requires the solution of only one parabolic equation. However, while the Darcy's model produces accurate results within the porous medium, it fails to provide satisfactory results in the free-flow regions of the karst aquifers. We propose a sector modeling approach to model fluid flow in karst aquifers. The sector modeling approach takes advantage of both the computational inexpensiveness of the Darcy's model and the accuracy of Brinkman's model in the caves. This method was compared to the Brinkman's model and the Darcy's model. Three examples are presented to study the effectiveness of the sector modeling technique. The first example is a simple model consisting of a straight conduit surrounded by porous regimes on either side while the second and third examples are more complicated structures consisting of complex geometrical caves embedded in a highly heterogeneous porous medium. Results show that the sector modeling approach provides an excellent match to the Brinkman's model and with much faster computations. Specifically, sector modeling was 4.6 times faster than the Brinkman's model in the first example, 13 times faster in the second; and 25 times faster in the third example. We also observed negligible deterioration in accuracy of results from sector modeling when the size of the extracted sector relative to the full-field is reduced.

Summary In this paper two different 2D synthetic reservoirs have been used to model tracer transport to better understand the geological structure of the reservoir, by sequentially solving Brinkman’s equation followed by the advection-diffusion-adsorption equation using the cell centred finite volume approach. The use of Brinkman’s equation is motivated by the fact that it simplifies the numerical modelling by allowing the use of a single equation to model the effect of both free flow and porous regions, thus in effect reducing the error due to improper modelling of the interface between the two regions. The same problems were also solved using Darcy’s equation for flow modelling, by using highly contrasting permeability values between the two regimes, this served as the control case used for comparison of the results.

The Brinkman’s equation simplifies the numerical modeling of karst aquifers by allowing the use of a single transport equation to model the flow of fluids in both the free-flow and porous regions, in effect reducing the error arising from improper modeling of the interface between the two regions. Most equations available to model flow within karst aquifers deal with steady flow conditions. This may not be accurate in aquifers where unsteady conditions exist. We considered the effects of unsteady flow conditions in karst aquifers by assessing the addition of an unsteady flow term to the Brinkman’s equation. We solved the coupled mass conservation-transport equations that models unsteady fluid transport in karst aquifers and studied the effects of unsteady flow conditions on tracer transport in two different sample aquifers and compared to the results obtained from the steady flow Brinkman’s equation. The solution method adopted is sequential and it involves solving the unsteady Brinkman’s model first, followed by the advection-diffusion-adsorption equation using the cell-centered finite volume approach. The first example presented here is a simple aquifer model consisting of a single conduit surrounded by porous regions. The second example is a complicated structure consisting of complex geometrical caves embedded in a highly heterogeneous porous media. The results show that, inside the caves, the unsteady Brinkman’s model yielded lower tracer concentrations at early times when compared to the steady flow model. At longer times, both models produced almost similar results. In particular, the results obtained from the simplified example case (Example 1) indicate that the velocity profiles for unsteady flow within open conduits do not instantly yield a parabolic shape expected from the Brinkman’s equation, but gradually develops into one starting from a linear profile. Results obtained also show that the addition of unsteady flow term to the Brinkman’s model does not affect the flow of tracer within porous media in any significantly observable manner.

Summary The Brinkman’s equation simplifies the numerical modelling of karst reservoirs by allowing the use of a single transport equation to model the flow of fluids in both the free flow and porous regions, in effect reducing the error arising from improper modelling of the interface between the two regions. However, most of the equations available to model flow within karst reservoirs deal with steady flow conditions. This approach however may not be accurate in reservoirs where unsteady conditions exist. We considered the effects of unsteady flow conditions in karst reservoirs by adding an unsteady flow term to the Brinkman’s equation. We solved the coupled conservation-transport equations that models unsteady fluid transport in karst reservoirs and then studied the effects of unsteady flow conditions on tracer transport in two different sample reservoirs. The solution method adopted is sequential and involves solving the unsteady Brinkman’s model first, followed by advection-diffusion-adsorption equation using the cell-centred finite volume approach. The same problems were also solved using a steady flow Brinkman’s model, and the results obtained were compared. were compared. The results show that, inside the caves, the unsteady Brinkman’s model yielded lower tracer concentrations at early times when compared to the steady flow model.

Abstract As the majority of conventional reservoirs are reaching maturity, the attention is gradually shifting towards unconventional and heavy oil reservoirs. Steam flooding, Miscible/Immiscible CO2 flooding, Polymer flooding and Water Alternating Gas (WAG) are the most common EOR techniques currently being employed in the industry. However, it has been recently proved that Polymer alternating gas (PAG) is a better alternative to the other EOR processes as it provides improved sweep efficiency (Zhang et. al. 2010, Li and Schecter, 2014, Kong et. al., 2015). A PAG process alternately inject miscible CO2 and water mixed with polymer. Different parameters directly affect the performance of this method. Thus, it is important that these parameters are carefully selected to increase the recovery along with the profitability. In this paper, an attempt has been made to optimize a PAG process for five production and five injection wells using two different global optimization algorithms. One injection cycle of a PAG process constitutes of two stages – (i) Injection of miscible CO2 (ii) Injection of polymer. CO2 dissolves in oil, swelling it and decreasing the viscosity thereby increasing the mobility of oil. The injection of polymer increases the sweep efficiency and reduces viscous fingering. In this paper the operational parameters that have been selected for optimization are well locations, number of injection cycles required, production BHP, CO2 injection rate, CO2 injection time, polymer injection rate and polymer injection time and the concentration of the polymer that needs to be injected. The algorithms used are the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and the Particle Swarm Optimization (PSO). A synthetic reservoir model was used to study the performance of the method. The Net Present Value (NPV) was used as the objective function to gauge the suitability of the solution and hence optimize the different parameters to obtain the highest NPV. Both used optimization algorithms yielded NPV that were well within the acceptable range. Additionally, sensitivity studies were conducted which showed that PAG gave a higher recovery efficiency when compared to the other EOR processes. Although some case studies have been performed to show the performance of the PAG process, to the best of our knowledge, no studies have been conducted on the use of global optimization algorithms for the estimation of the operational parameters of the PAG process.

Abstract Determining the optimum well locations while developing a field contributes significantly to efficient reservoir management. Most optimization problems have focused on maximizing the Net Present Value (NPV) of the project. However, the ultimate aim of projects differs between international and national oil companies. The primary aim of international oil companies (IOCs) is to maximize its profit (NPV). As far as a national oil company is concerned, apart from maximizing NPV one of its main objective is to sustain its resources for as long as possible by delaying abandonment i.e., national oil companies (NOCs) aim to increase the field recovery factor (RF) by maintaining a long period of plateau production. Therefore, a multi-objective approach is presented which incorporates both NPV and RF as objective functions in well placement and rate optimization of Brugge field. The technique of Particle Swarm Optimization (PSO) was used to solve the optimization problem. The search for optimum well locations was conducted in three stages. In the first stage, only NPV was used as the objective function while in the second stage it was only RF. In the third stage, objective function was a weighted sum of NPV and RF in which a set of three weights was used to describe the relative importance of NPV and RF. A comparison of how these weights affect the optimized NPV and RF values is presented. The approach quantitatively clearly differentiates between the operating strategies of NoCs and IoCs and shows how to balance between maximizing profitability and keeping RF as stable as possible. The method takes care for both IoCs and NoCs to optimize the financial aspect (NPV) and the energy sustainability aspect (RF) of field development projects. This technique presents a realistic NPV estimation while keeping a balance in RF for both companies.

Summary In well placement optimization, simultaneously satisfying the investors and environmental agencies is difficult because enforcing environmental regulations have negative impact on economic returns. Thus, a balance between ensuring profitability and keeping to environmental regulations must be found. The NPV is often used as an indicator of economic performance while the VRR is one of the factors used as an indicator of environmental safety. Recently, a study was conducted proposing the use of a weighted combination of the NPV and VRR as an objective function in well placement optimization. This approves the importance of considering both objectives. The approach however, requires that users determine priori the weight of each objective. In this work, we present a Pareto-based multiobjective approach for integrating the NPV and the VRR in a well placement optimization framework. This approach allows us to obtain several optimal instances of well placement, each instance optimal in a particular sense. Differential evolution was used as an optimizer and well spacing constraints were enforced. A set of Pareto-optimal well locations was obtained. A synthetic case is presented to show the usefulness of the approach and analyses of how investors can use these Pareto-optimal well locations in field development planning are presented.

Summary Deciding the life cycle of field development projects is one of the problems that routinely face reservoir asset managers. In this work we investigate the effect of production time in well placement optimization as well as the effect of adding well spacing constraints. To make the investigations a synthetic heterogeneous reservoir example was used, and Covariance matrix adaption evolutionary strategy (CMA-ES) coupled with numerical reservoir simulator was used as an optimization tool. The optimization was made on the locations of oil producers and water injectors as well. Two cases were considered; in the first case there were no constraints while minimum well spacing was defined in the second case. To study the effect of production periods in the optimization three classifications were used; short term, medium term and long term, and so short term NPV, medium term NPV and long term NPV set as different objectives functions for each of the mentioned terms respectively. We proved that the optimum well placement for specified project period is not necessarily the best for different period. Adding well spacing constraints to the optimization significantly affected the solution, which shows the importance of selecting the proper minimum well spacing to build fields’ development plans.

Abstract Naturally Fractured Reservoirs (NFR) hold a significant fraction of remaining petroleum reserves. Recovery factors from NFR are usually less than in conventional reservoirs due to associated high uncertainty throughout the characterisation and modelling phases. This particularly includes the modelling and upscaling of the fracture domain using Discrete Fracture Networks (DFN). Computer assisted history matching and prediction is becoming increasingly popular as they help finding multiple history-matched models and probabilistic forecasts. Therefore, the associated uncertainty can now be quantified in a limited time frame. However, the results of a history match are known to depend on initial reservoir properties, including fracture permeability and matrix shape factors. Geological uncertainty in these two factors is exacerbated by the DFN upscaling errors. We show how DFN modelling can be used to increase geological prior knowledge and hence produce more geologically consistent models. To highlight DFN upscaling errors, we use a realistic dataset from an onshore fractured reservoir to show how the DFN upscaling error could propagate through to the history matching phase. We compare history matching of three models with different DFN upscaling processes. Results from state-of-the-art assisted history matching and prediction were found to depend on the static properties and particularly the computation of effective fracture permeability during DFN upscaling. This upscaling error alone leads to very different reservoir models, despite the best history matched models being of comparable quality. Hence, this leads to more uncertainty in reservoir production forecast. The identification of DFN upscaling errors is therefore crucial for better uncertainty quantification in reservoir simulation of NFR.