This article focuses on interpretation of well test data from wells completed in naturally fractured reservoirs. Because of the presence of two distinct types of porous media, the assumption of homogeneous behavior is no longer valid in naturally fractured reservoirs. This article discusses two naturally fractured reservoir models, the physics governing fluid flow in these reservoirs and semilog and type curve analysis techniques for well tests in these reservoirs. Naturally fractured reservoirs are characterized by the presence of two distinct types of porous media: matrix and fracture. Because of the different fluid storage and conductivity characteristics of the matrix and fractures, these reservoirs often are called dual-porosity reservoirs.

Individuals who have dedicated their careers to the advancement and improvement of the oil and gas industry and have made significant contributions to it are considered Pillars of the Industry and are featured in this section. In this issue, Abbas Firoozabadi of the Reservoir Engineering Research Institute and Yale University and Hussein Hoteit of ConocoPhillips engage in an interesting review of traditional ("old school") numerical-simulation approaches and explain the need to change (to "new school") reservoir-simulation approaches. They seek to spark interest among young professionals and the industry at large in embracing a new line of attack for complex reservoir-simulation problems. In a second article, Ganesh Thakur of Chevron explains how technical professionals in our industry are being challenged to develop and apply innovative ("new school") solutions to emerging, complex, and multifaceted problems that typically require much more than the sometimes outdated ("old school") industry-standard practices. Thakur emphasizes the strategic importance of technical professionals to our industry and elaborates on what is required from industry to retain this much-needed talent.

The Journal of Petroleum Technology, the Society of Petroleum Engineers' flagship magazine, presents authoritative briefs and features on technology advancements in exploration and production, oil and gas industry issues, and news about SPE and its members.

A great deal of computational power and time is necessary for simulating highly heterogeneous fractured reservoirs with complex geometry; the efficiency of these computations is a major subject especially in large-scale heterogeneous and fractured reservoirs. So, simulating large scale, complex and fractured reservoirs with both minimum time and maximum accuracy is the scope of current work. The BiCG-Stabilized solver preconditioned by CPR-AAMG has been developed to achieve acceptable results of high efficiency and robustness for large heterogeneous fractured Black-Oil models. The solver's efficiency is demonstrated in an Iranian fractured field model with heterogeneity. As an observation, the NF preconditioner which is embedded in ECLIPSE has low efficiency in simulating giant fractured reservoirs. On the other hand, the advantageous efficiency of the proposed CPR-AAMG preconditioning technique increases with the complexity of the model. The findings indicated that the CPR based preconditioners have better performance than classical ILU based preconditioners according to the number of linear solver iterations in RETINA. CPR-ILU0 is the most efficient preconditioner, based on speed and calculation cost (numbers of linear solver iterations). RETINA can take larger time steps in comparison with ECLIPSE, and therefore has better elapsed time. Comparison with the results of other preconditioners developed in ECLIPSE approved that the developed CPR-ILU0 in RETINA is robust. The main conclusion would be that RETINA has an exceptional and stable performance for simulating highly heterogeneous fractured models. The novelty of the proposed CPR-AAMG preconditioner is the ability to solve three dimensional black-oil models of giant, complex and fractured reservoirs with reduction in time and required computational power.

Polymer gel technologies have been widely employed in conformance-control applications to improve sweep and recovery from high permeability reservoirs. Modeling of polymer gel propagation, gelation time, and adsorption in the porous media requires a complex full-order computation of diffusivity equations, heat transfer and chemical reactions for components partitioning in oleic and aqueous phases. This paper presents a soft-computing alternative and data-driven approach to using machine learning to model deep polymer gel treatments in fractured reservoirs.

Reference simulation models of various dual-permeability systems were used to generate the dataset for the machine learning model, an artificial neural network. A second-order reaction scheme for gel formulation was used to describe the chemical reaction between the polymer, polyacrylamide, and the cross-linker component, chromium acetate. Viscosities, adsorption properties and residual resistance factors of the polymer and produced gel were populated based on experimental data. Various conformance design factors and reservoir properties were parameterized for inclusion in the neural network, including: temperature, injection rate, gel concentration, bottom-hole pressure, drainage radius, porosity, fracture spacing and permeability.

Feature analysis of the input variables indicated that ten parameters are sufficient to train the model and predict the performance of the conformance treatment with indicators including the oil and water rate profiles improvement after applying the polymer gel treatment. The dataset was randomly divided into 80% training, 10% validation and 10% testing sets. Early stopping and monitoring of the validation and testing set's errors were used to generalize the solution and enhance the performance of the neural network. Hyperparameter tuning showed that using a deep multi-hidden layers neural network was more effective than increasing the neurons in a single hidden layer. The weights and biases of the model were adjusted using a mean squared error loss function and a gradient descent optimizer. A correlation of 90% was achieved for the test samples with a mean absolute deviation of less than 10% for all modeled variables. The developed neural network model was able to reduce the complexity of the full-order simulation model by accurately predicting the performance of polymer gel treatments in fractured reservoirs at 200 times faster speed than commercial simulators with only ten input variables of polymer gel design and reservoir properties.

This work presents a unique surrogate modeling approach based on machine learning to describe complex polymer gel kinetics and flow dynamics in deep conformance applications. The presented neural network model can be used to robustly predict the oil recovery performance of polymer gel treatments in fractured reservoirs outperforming commercial simulators in terms of computational complexity and processing speed.

Fractures are common features of many carbonate reservoirs. Given complex flow network that they create, characterization of dynamic behavior of these reservoirs is often complicated and becomes important, especially, if fractures provide primary pathways of fluid flow. In this paper a novel semianalytical simulator was used to understand the pressure behavior of naturally fractured reservoir containing a network of discrete and/or connected finite and infinite-conductivity fractures.

In this study an integrated interpretation methodology is applied to analyze well test data acquired in open hole section of exploration well drilled into highly fractured carbonate reservoir of Lower Eocene - Upper Cretaceous sediments on Patardzeuli field of Block XI-B, Republic of Georgia. The main steps consisted of explicitly modeling fractures - both wellbore-intersecting fractures and fractures located away from wellbore - using formation microimager data and calibrating the model to actual well test response using a unique novel mesh-free semi-analytical simulator designed for fractured reservoirs.

Study presents the results of well test of one zone performed in highly fractured carbonate reservoir drilled in Patardzeuli field. The pressure-transient response confirmed the complexity of reservoir and dominant contribution to flow regimes from fractures.

It is shown in this paper that there are many factors that dominate transient behavior of a well intersected by natural fractures, such as fracture conductivity, length, intensity and distribution, as well as whether fractures intersect the wellbore or not. Moreover, it was demonstrated that presence or absence of damage on wellbore-intersecting fractures in vicinity of wellbore will impact the pressure- transient behavior of reservoir and shape overall productivity of the well.

The novelty of the approach is the analysis of the dynamic behavior using a unique semi-analytical pressure transient simulator for fractured reservoirs. The simulator can be used to obtain a response for arbitrarily distributed infinite and/or finite conductivity natural fractures within the reservoir by modeling them explicitly. In this study, it allowed to maximize the value of well tests by assessing the effect of fractures on reservoir dynamic behavior and obtain matrix and fracture parameters where conventional well test interpretation tools would be deemed unviable.

The paper discusses the conceptualization, design and implementation of an interwell waterflood passive chemical tracer study in stacked naturally fractured carbonate reservoirs, in a Field in the Sultanate of Oman. The reservoir structure is an almost undisturbed directionally trending anticline, intercalated by several fault zones at the southern flank, running parallel to the structural axis. The shaly intercalations intercepting the chalky reservoir add another layer of complexity to the fractured reservoir system.

During the 1960s, the field was first put on fracture spurt production which then rapidly declined by the 1970s. Thereafter, a peripheral water injection program was introduced followed by a gas oil gravity drainage to stabilize production rates. To manage the uncertainties arising out of the complex geology and develop the field in the best techno-economic fashion, it was imperative to gain understanding of the fluid flow dynamics operating within the reservoir that was dominated by the complex fracture-matrix system. Over decades, chemical tracers have been a proven and reliable source to gather such information.

Over the past years, several studies using interwell passive water tracers were conducted in the field, covering various areas of interest. The design of the tracer study in a fractured system is more complex than designing the same for simple hydrodynamic applications. Complexities including preferential fluid influx through layered reservoirs, competing fracture matrix movement and relatively faster breakthroughs amongst other things required to be considered. Connectivity between overlaying reservoirs was suspected to affect flow dynamics but not confirmed in water flooded areas. In this case, another layer of complexity was added due to re-injection of produced water that required meticulous understanding of the pseudo breakthroughs. The paper discusses the challenges in design, implementation, interpretation and simulation when carrying out a passive chemical tracer study in a fractured reservoir and the best practices adopted to counter them. The case study also discusses immediate oil gain, the qualitative and quantitative results obtained from two large scale tracer studies involving fourteen injectors and 36 producers and six injectors and nineteen producers carried out in recent years and explains the results in light of mean residence time, swept volume, sweep efficiency and heterogeneity indices. Additionally, the study also discusses numerical tracer simulation modelling.

To date, available literature only explores the concept of complex tracer studies in fractured reservoirs through numerical simulation models. To the best of the authors' available information, this study is pioneering in the provision of best practice guidelines when designing such a study with respect to volume assessment, operational challenges, design of customized sampling and analysis plans, qualitative and quantitative interpretation of the data obtained as well as modelling aspects.

For speeding up the complex fractured reservoir simulating, we have given more attention to reducing runtime and improving efficiency of the solver. In this work, we describe an improved and computationally efficient version of Newton's method, which reduces the non-linear iteration count, increases time steps, and furthermore reduces time spent in nonlinear loops of reservoir simulating. Safeguarded variants of Newton's method which have used in current reservoir simulators cannot guarantee convergence of the solution, especially in highly heterogeneous, detailed and fractured reservoirs. In such simulators time step chopping is often observed. From other hand, with growing complexity, convergence difficulties can lead to considerable losses in computational effort and prohibitively small time steps. For overcoming this problem, an improved and computationally efficient version of Newton's method, which uses higher order terms in the Jacobian matrix in addition to the Newton's basic linear terms to account for cross variable dependencies including a high-tech stable linear solver, is proposed. This scheme leads to a smaller number of non-linear iterations compared to other known commercial simulators with larger time steps. In addition to SPE10 model, an under saturated fractured black-oil reservoir model, including single and dual grid blocks together, with dimension of 124*105*60 grid blocks is used to evaluate the method efficiency, by comparing the results using the known commercial simulator. This heterogeneous model includes the features of hysteresis, gravity drainage, oil-gas surface tension, and moderate aquifer with 8 active production wells which have produced for 24 years. The numbers of time steps are 525 and 2693 for proposed method and the commercial simulator, respectively. The numbers of Newton iterations are 1667 and 9399, and the average time step size is 26.2 and 5.62 days, for proposed method and the commercial simulator, respectively. These results obviously indicate the efficiency of the proposed method in reducing Newton's iterations and accordingly increasing simulation speed. The novelty of the proposed creative method is in reducing non-linear Newton's iterations and increasing time steps which leads to reduced simulating elapsed time, which has never been observed in current simulators.

Our studies undertaken at many oil and gas fields in different basins show that fractures separate reservoir rocks into differently-sized blocks that are complex self-similar fractal structures whose behavior is described by Pareto's common universal law. Based on this law, a fractal model of fractured reservoir was developed. It includes several hierarchical levels of matrix blocks and fractures, sometimes ten and more. In the proposed model, not only the sizes of the blocks are in the ratio of 1.618, and permeability of the fractures changes in the ratio of 1.618, which allows to reproduce the daily and cumulative oil and gas well production according to power law distribution and Pareto's law. According to the laws the article deals with one of the development paths which we proposed to call "intensive" [15]. Currently, this path of development is almost ignord by oil and gas companies, which, in order to increase the capitalization of their assets, are aimed at using modern digital technologies, using the capabilities of artificial intelligence, big data, neural networks and machine learning, etc. However, we believe that the path can make a significant economic and environmental contribution to the development of hard-to-recover resources in tight fractured carbonate reservoirs. The proposed development path is based on an understanding of the "smart" nature phenomenology and training of modern creative professionals in the base oil and gas universities. This development path allows to substantially reduce expenses while obtaining higher daily and cumulative production of hydrocarbons and preserving the natural potential of fractured reservoirs created by the nature itself. Today's specialists working on development of information technologies call this development path "nature-like technologies". However, considering natural fractured oil and gas reservoirs, we can talk about a purely natural phenomenon.

The gravity drainage process is one of the essential recovery mechanisms in the naturally fractured reservoirs. The contribution of the process to the ultimate oil recovery is quite uncertain, and it highly depends on the mathematical models that used in representing the process besides matrix characteristics such as shape factor and matrix block dimensions in addition to the matrix permeability. The fluid exchange rate between the matrix and fractures is the main controlling factor on the oil recovery, as most of the oil reserve stored in the matrix. Therefore, appropriate gravity model selection supported by accurate matrix characterizations can enhance the simulation accuracy and to avoid an overestimation to the oil recovery.

In this work, an outcrop-based model was used to provide a realistic representation of the fracture network in a dual-porosity model. The constructed fracture model was employed to assess the impact of the gravity drainage mechanism. The investigation comprises several sensitivity scenarios and cases to evaluate the influence of both mathematical models and matrix properties using an intermediate resolution model with a single producer located the grid centre and a natural depletion scenario.

The simulation results indicated remarkable differences in the producer's performance and productivity. The variation in performance is purely mathematical and related only to the gravity drainage options. Furthermore, the sensitivity results highlighted the significant impact of the matrix characteristics on the fluid exchange between the matrix and fractures, hence oil recovery. Therefore, misunderstand the impact of the mathematical models, and the influence of the matrix properties could result in a compound error in predicting the reservoir performance and its recovery, hence making an inappropriate development decision.