This paper describes three applications of a fully integrated hydraulic fracturing, reservoir, and wellbore simulator. The simulator describes hydraulic fracturing, shut-in, and production in a single continuous simulation. It describes multiphase effects (using either the black oil model or a compositional fluid model), thermal effects, transport of tracers and/or non-Newtonian fluid additives, stress shadowing from fracture propagation, and poroelastic stress effects from depletion, and uses a detailed proppant transport algorithm. It uses constitutive relations that smoothly transition from equations for flow through an open crack to equations for flow through a closed crack (with or without proppant). In the first example, we build a simulation model of Staged Field Experiment #3, a well-known historical dataset. Our result is compared with other published simulations and is matched to 15 years of production data. The simulation shows how the transport and settling of proppant in the fracture during injection and shut-in are impacted by processes such as clustered and hindered settling. Gel crosslinking and breaking are described with first-order reaction rate constants. In the second example, we perform a sensitivity analysis on cluster spacing in a generic slickwater fracturing treatment in a horizontal well. The simulations show complex interactions between stress shadowing, fracture propagation, proppant transport, and multiphase flow. The sensitivity analysis indicates that minimizing near-wellbore pressure drop is critical for improving production. Closer cluster spacing decreases near-wellbore pressure drop by providing more conduits for flow. In the third example, we simulate a vertically stacked parent/child scenario. Depletion of the overlying parent well leads to upward propagation from the child well and direct frac hits. The frac hits remobilize proppant as water sweeps into the parent well fractures, displacing gas. In the
The use of acid is an important well maintenancetool in removing near wellbore damage to restore a reservoir's natural permeability and represents one of the most economic options in managing base decline. The selection of acid maintenance candidates however can be a complex process, particularly in wells completed across multiple sands, involving many factors both on the surface and subsurface. As a consequence, individual acid maintenance jobs have had a mixed success rate historically, with certain jobs resulting in a lack of response, or worse, higher water production rates and equipment failures.
This paper uses the Wilmington Oil Field, located in southern California, as a case study to examine the typical characteristics associated with low volume acid maintenance success and provides a novel approach using machine learning (ML) algorithms to aid in the screening and selection of future candidates. The developed algorithms, which make use of the open-source statistical software R, is trained based on results from over 500 producer and 3900 injector acid maintenance jobs that were executed at the field and incorporates predictors from the following groupings determined from literature and subject matter experts (SMEs): Production/injection history, Reservoir properties, Acid type and volume, Delivery mechanism, Formation damage, Well completion design, and Surface facility properties. Over 100+ predictor variables were compiled and screened using supervised feature selection to identify those variables providing the greatest explanatory power. A series of machine learning models: Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)) were then used in a classification task to successfully predict whether a producer acid maintenance job would be economic.
The logistic regression model ultimately yielded the best classification results (71% prediction accuracy for the producer jobs and 77% for the injector jobs) and proved to be the ML algorithm with the best balance of accuracy, interpretability, and ease of implementation in the field. The model robustness is examined by applying the algorithm outside of the training and test datasets, to acid maintenance jobs executed in 2016-17 and shows similar predictive accuracy. As a result the model is being actively used to automatically screen for treatment candidates among all 700+ producers and 400+ injectors in the Wilmington Field, which are then validated by SMEs before being executed. The overall process has resulted in significant cost savings by both improving the performance of the acid maintenance program and greatly reducing the amount of time spent by technical staff in selecting candidates. These results indicate that ML algorithms can be effective analytical tools not only for ‘big data’ problems (i.e. largen, time series datasets) which are featured heavily in industry literature, but also for smaller datasets thus opening up a variety of potential applications that can be deployed by surveillance teams alongside traditional approaches.
The conventional method for describing transport of solutes such as salt, polymer and tracers is to use the mass conservation law. In simulation of this law for Low Salinity Water Flooding (LSWF), the effective salinity range defines how salt affects the mobility of water and appears to be crucial for controlling fluid movement. In this study, we examined the non-linear feedback between salt concentration movement and the low salinity water front as a function of physical and numerical dispersion, in combination with the effective salinity range, and we investigated how the front speeds were altered.
We examined a numerical model of the mass conservation law to simulate LSWF at the reservoir scale. The cell sizes and the time steps were chosen to control the numerical dispersion coefficient in place of physical diffusion. A range of diffusion coefficients was considered along with various representations of the effective salinity range and the function that controls the effect that salt has mobility. The latter has been shown to be variable in the literature. We compared simulations to the analytical solution of solute transport obtained for the diffusion-advection equation assuming a fixed flowing velocity.
We observed that the salinity front moved faster than was predicted by the analytical solution and this effect was increased the further the effective salinity range was set below the connate water salinity. In this case, the higher salt concentrations lay in the faster moving water (the connate water front), which also speeded up. This was very much a dispersion related effect, with the variation of velocity growing as the salt concentration spread out. By implementing many numerical tests, we obtained a modification to the advection term in the conventional mass conservation law of solute transport. This term depends on the Peclet number, the velocities of the high and low salinity fronts and the effective salinity as a proportion to the connate water salinity. In an advection-diffusion system, these factors usually affect only the advection term (the front velocity), while the diffusion term is unchanged.
From numerical tests, we can now rapidly predict the movement of the salt front by this newly derived modification of the analytical solution.
A robust high precision experimental approach to determine dew point pressure of gas condensates in the laboratory is proposed in this study. Gas condensate reservoirs have been the center of attention for numerous numerical and experimental studies for decades. Their perplexing fluid flow and phase behavior results in various production challenges including condensate banking and compositional changes due to retrograde condensation accompanying production from these reservoirs. Therefore, accurate prediction of dew point pressure (DPP) is crucial in developing long-term production plans for these reservoirs.
Isochoric method, an indirect high precision way of DPP and phase transition condition determination, is commonly used in other disciplines where a clear non-visual determination of phase transition of a fixed volume of fluid is needed. This study provides an insight into this approach in determining DPP for a binary mixture of hydrocarbons. A semi-automated apparatus for measuring and monitoring equilibrium conditions along with fluid properties is designed based on the isochoric method. The apparatus provides constant volume, variable pressure (0 to 1500 psi), and variable temperature (290 to 410 K) experimental conditions. Pressure and temperature measurements are used to detect the phase transition point along the constant mole-constant volume line based on the change in the slope of this line at the transition point.
Results are plotted on the phase envelope (P-T diagram) of the same mixture using different equations of state and the accuracy of each of these equations of state in providing the most reliable prediction of DPP is analyzed. Reproducibility of the data is examined and error estimation for the entire experiment is provided. This experimental method is inexpensive, less time consuming, and more accurate compared to other PVT experiments and is applicable for multicomponent systems. It does not require gas expulsion or sample recombination throughout the procedure and could be identified as the only reliable way of quantifying the effect of porous media on phase behavior.
The use of data-driven cognitive solutions represents a major advancement in the management of oil and gas operations. Tools that integrate concepts from disciplines such as informatics, machine learning and predictive analytics can offer powerful solutions to allow improvements in the safety, efficiency and integrity of oil and gas operations. We discuss data systems that enhance traditional, human-based monitoring systems, with the aim of approaching risk-free operations.
Ethical decision-making is critical in the management of oil and gas operations. Digital data solutions effectively compensate for the limitations of human-based decision processes when confronted with data overload and multi-dimensional data systems. Increased adoption of data gathering, automation, data analytics and advanced computer-aided process control has already made its mark in the industry. Examples have emerged as for how in areas such as artificial lift, pipeline transportation and offshore operations, these data analytics techniques have helped in failure detection and prediction and smart management of such operations. The incorporation of data-intensive decision-making and smart risk management solutions have resulted in a step change in the improvement of the ethical foundation and the base underlying the industry. Moreover, these digital tools and machine-based cognitive processes for risk-avoidance solutions can help to build and restore the public's faith and trust in the industry. We also discuss how relying on digital solutions alone can have its limitations when it comes to professional ethics and responsibilities.
The oil and gas production landscape in North America has seen a paradigm shift since the collapse in oil prices in 2014. Although prices remain challenging, several operators have managed to sustain the relatively long period of low margins through some aggressive approaches. This paper inspects changes in operating strategies and field development plans across all oil-rich basins in the US Rocky Mountain fields and how operators have used a combination of low oilfield service prices, high-graded well locations, and incremental fluid/proppant volumes to increase production.
The paper investigates the transformation in operating philosophies since 2014 in four oil-rich basins in the Rocky Mountain region—Williston, Denver-Julesburg (DJ), Uinta, and Powder River. The Bakken formation in the Williston basin represents one of the best-quality rocks in all of North America. However, high oil-price differentials and well costs have made it difficult for drilling to remain profitable. The core of the DJ basin (Wattenberg) has one of the lowest break-even prices in the region, and rig count continues to increase as operators start seeing signs of recovery in the market. The Uinta basin, although relatively small in size, has shown tremendous return potential in the form of multiple stacked pays and promising production results. The Powder River basin poses one of the toughest operational environments in the region owing to wildlife stipulations, harsh weather, and deeper targets.
High-graded well locations in the Bakken are limited to few fields, which limits the scope of expansion in the current oil price environment. The DJ basin is challenged with high-density well spacing; estimated ultimate recovery (EUR) per drilling spacing unit (DSU) continues to increase, but EUR per well has gone down by as much as 60%. In the Uinta basin, formations never known to be continuous in the Green River group have shown significant return potential. The Powder River basin has recently attracted large investments from major independent operators as they tackle drilling challenges associated with abrasive rocks and testing optimum lateral landing points.
Case studies show how operating strategies have changed with changes in oil prices. The Bakken and DJ basins are relatively mature, and as drilled-but-uncompleted (DUC) inventory continues to increase, depletion from existing wells and interference between fractures is impacting production from new wells. The Powder River basin is still in the exploratory phase, and operators are still working on reducing well-costs, optimizing fracturing-fluid/proppant volumes, and examining productivity of other target rocks. The Uinta basin is in the early phases of expansion, with many of the fields still being explored for scalability. Changes in production maps and completion trends provide a comprehensive understanding of how these variables have impacted oil output from the region since 2012.
This paper presents a theoretically rigorous formulation and correlation of the effect of poroelasticity on stress-dependent petrophysical properties of naturally-fractured reservoirs, including porosity, permeability, relative permeability, and capillary pressure, by consideration of the stress shock effect across a critical effective stress and the pressurization/depressurization hysteresis. This model accounts for the deformation, transformation, deterioration, and collapse of the pore structure during pressurization and depressurization processes and their effects on the properties of naturally-fractured reservoir formations. A stress shock is shown to occur in naturally-fractured reservoir formations at a critical stress during transition between open and closed natural fractures in loading and unloading applications. The effect of the stress shock and pressurization/depressurization hysteresis on petrophysical properties of reservoir formations is formulated by means of a modified power-law equation derived from a phenomenological model referred to as a rate equation. The modified power-law equation is shown to alleviate the shortcomings of the ordinary power-law equation applied in many studies.
The comprehensive model developed in this study is validated by means of various experimental data gathered by testing of samples from sandstone, carbonate, and shale reservoirs. The phenomenological parameters of the rock samples are determined for best match of experimental data. The scenarios examined in this study indicate that pressurization/depressurization hysteresis has a significant effect on the stress-dependent porosity and permeability of reservoirs. The model developed in this paper can describe the stress-dependent porosity and permeability of the fractured rock formations much more accurately than the commonly used empirical correlations. The accurate methodology presented for proper correlation of stress-dependent properties of reservoir formation rocks honors the slope discontinuity at a yield or critical effective stress. The stress-dependency of rock properties are described by the modified power-law expressions separately over the low stress region below the critical stress and the high stress region above the critical stress. The proposed data correlation methodology is proven to be highly effective in the analyses and correlations of the experimental data of various types of reservoir rock formations as indicated by the correlations achieved with significantly high coefficients of regressions very close to the unity.
The analysis of previous (offshore) oil and gas drilling accidents indicates that Human and Organizational Factors (HOFs), in addition to technology- and work processes-related elements, play a critical role in contributing to those accidents. These HOFs are originated from different layers of key involved decisions-makers, both internal and external to an organization. This paper proposes a system-oriented methodology for the risk analysis of oil and gas drilling industry by integrating the two powerful frameworks of AcciMap and Bow-tie.
In the first phase, the AcciMap framework, which was originally proposed by Professor Jens Rasmussen in 1997, is used as a systematic accident investigation methodology for the analysis of the BP Deepwater Horizon (DWH) blowout, as a case study. This graphical representation, by incorporating associated socio-technical factors into an integrated framework, provides a big-picture to illustrate the context in which an accident occurred as well as the interactions between different levels of the studied system that resulted in that event. In the next phase, the results from analyzing the BP DWH accident using the AcciMap framework are used as a foundation for the development of a Bow-tie model. This model, as a barrier-based risk assessment framework, introduces both preventive and mitigation barriers that by incorporating them into an analyzed system, respectively, the likelihood of occurrence of future accidents and their negative consequences will be reduced.
The analysis of the BP DWH accident indicates that aside from influencing external components, organizational factors such as economic pressure as well as communication and interoperability issues, both in regular and emergency situations, were the root contributing causes of this accident. This analysis is then used for the identification and development of appropriate barriers in our proposed Bow-tie framework. In addition to organizational factors, human factors and technological elements have contributed to this accident and other cases. Those are also identified and used to develop approporiate barriers in our Bow-tie framework.
The integration of the two frameworks of AcciMap and Bow-tie, which are compatible with each other and complement each other, provides a novel comprehensive perspective to better analyze operations in high-risk systems such as the oil and gas drilling industry. It is noteworthy that although the utilized AcciMap framework was developed for the analysis of the BP DWH blowout, our defined preventive and mitigation barriers in our Bow-tie framework are generalized to represent other (offshore) oil and gas drilling cases as well. Therefore, our proposed integrated methodology can be used for the risk assessment of operations in the entire (offshore) oil and gas drilling industry. Furthermore, this paper and its proposed methodology provides a roadmap that can be adopted by any safety-critical industry; e.g. petrochemical processing, transportation and nuclear power, for the risk analysis of its operations.
Naturally fractured reservoirs constitute a significant portion of oil and gas fields worldwide. Like all reservoirs, waterflooding is routinely used in naturally fractured formations to increase recovery. However, the benefits of waterflooding can be limited due to early water breakthrough via the fractures. Therefore it is imperative to closely monitor the flood progress in these reservoirs. Analyzing transient tests in water injection wells, especially early in the life of the flood can provide valuable information, such as the mobilities in various regions around the well and the location of the flood front. An analytical model to design and analyze falloff transient data in naturally fractured reservoirs is highly desirable so that pressure transient analysis techniques can be applied for monitoring and optimizing secondary recovery projects.
In this paper we present a semi-analytical solution for the pressure response during falloff tests in naturally fractured reservoirs under multiphase flow conditions. We consider water injection into an oil reservoir, resulting in two-phase flow. In our model, the radial variation of fluid saturation is modeled as a multibank reservoir with constant saturation in each bank. Each bank has a different relative permeability and compressibility value, corresponding to the fluid saturation in the bank. We model naturally fractured reservoir behavior using Warren & Root's dual porosity model, which is extended to accommodate two-phase and multi-composite reservoirs. We also include capillary pressure effects in the model.
The proposed semi-analytical solution was tested and compared against numerical simulation results obtained from commercial simulators. The results have been in excellent agreement, validating our semi analytical approach. Using the proposed solution provides a rigorous and fast method to design and analyze tests. It also allows for using nonlinear regression techniques as opposed to computationally expensive trial and error matching for estimation of reservoir properties. Our analytical model can also be used as a guideline for grid refining in the vicinity of the wellbore and time-step selection in numerical simulators for transient tests analysis.
We expect our analytical method will enable operators and engineers to design and analyze falloff tests quickly and accurately in naturally fractured reservoirs.
A pressure transient model to detect fault reactivation is presented in this paper. The central assumption of the fluid flow model is that fault permeability gets suddenly altered upon fault slip. The fault is modeled as a linear interface segmenting an infinite, homogeneous and isotropic reservoir into two semi-infinite regions. A constant-rate well induces pressure changes in the reservoir by either fluid withdrawal or injection that lead to fault reactivation. The mathematical model is solved via integral transforms and the analytical solution is examined at the well with the purpose to find the characteristic bottomhole pressure and pressure derivative response to a fault slip event. Typical diagnostic plots and type curves used in well testing analysis are presented. A reservoir characterization approach summarizes the application of the model presented in this paper.