The present study provides a comprehensive set of new analytical expressions to help understand and quantify well interference due to competition for flow space between the hydraulic fractures of parent and child wells. Determination of the optimum fracture spacing is a key factor to improve the economic performance of unconventional oil and gas resources developed with multi-well pads. Analytical and numerical model results are combined in our study to identify, analyze, and visualize the streamline patterns near hydraulic fractures, using physical parameters that control the flow process, such as matrix permeability, hydraulic fracture dimensions and assuming infinite fracture conductivity. The algorithms provided can quantify the effect of changes in fracture spacing on the production performance of both parent and child wells. All results are based on benchmarked analytical methods which allow for fast computation, making use of Excel-based spreadsheets and Matlab-coded scripts. Such practical tools can support petroleum engineers in the planning of field development operations. The theory is presented with examples of its practical application using field data from parent and child wells in the Eagle Ford shale (Brazos County, East Texas). Based on our improved understanding of the mechanism and intensity of production interference, the fracture spacing (this study) and inter-well spacing (companion study) of multifractured horizontal laterals can be optimized to effectively stimulate the reservoir volume to increase the overall recovery factor and improve the economic performance of unconventional oil and gas properties.
Temizel, Cenk (Aera Energy) | Tiwari, Aditya (Pioneer Exploration LLC) | Kirmaci, Harun (Turkish Petroleum Corp.) | Aktas, Sinem (Turkish Petroleum Corp.) | Ranjith, Rahul (University of Southern California) | Zhu, Ying (University of Southern California) | Tahir, Sofiane (ADNOC) | Aminzadeh, Fred (University of Southern California) | Yegin, Cengiz (Texas A&M University)
Optimization has become a practical component in decision-making processes in field development and reservoir management. Although optimization simplifies decision-making, it harnesses complex equations and formulations that may be computationally expensive to solve. Numerical reservoir simulation adds another dimension to this phenomenon when combined with optimization software to find the optimum defined by an objective function. Considering the fact that current reservoir simulation models are constructed with vast amount of data and if it is coupled with optimization software, computational limits of regular computers can cause not being able to reach the aimed result although the recent technological development allows running huge reservoir models with parallel computing systems. Consequently, it is inevitable to achieve robust and faster results in optimization problems.
Predefined objective functions in optimization software when combined with numerical reservoir simulators attempt to maximize the net present value or cumulative oil recovery defined with an objective function, where the objective function can be defined to be multi-objective leading to Pareto sets consisting of trade-offs between objectives. Using an optimization algorithm with predefined objective functions does not provide the flexibility to the physical reservoir fluid flow phenomenon to "maneuver" throughout the iterations of an optimization process. It is necessary to use a more flexible objective function by introducing conditional statements through procedures.
In this study an optimization software is combined with a commercial reservoir simulator. Conditional statements implemented in the simulator as procedures help the software/simulator combination operate under pseudo-dynamic objective functions that lead to speed and robustness through trying sets of combinations of parameters, and thus achieving conditions that lead to highest recovery within the given time frame as defined by the conditional statement for the condition for which the simulation run is performed. The procedures feature enables implementation of codes by using conditional statements that act as piecewise objective functions, maximizing the recovery and taking into account the timeframe or condition they belong.
A commercial reservoir simulator is used in this study with conditional statements enhancing production in a given timeframe featuring certain conditions. The optimized recoveries with pseudodynamic objective functions provide an enhanced recovery, as compared to that of an optimization case with predefined constant objective function in the optimization software throughout the iterations of the optimization and simulation process.
Temizel, Cenk (Aera Energy) | Tiwari, Aditya (Pioneer Exploration LLC) | Aktas, Sinem (Turkish Petroleum) | Putra, Dike (Rafllesia Energy) | Suhag, Anuj (University of Southern California) | Kirmaci, Harun (Turkish Petroleum) | Balaji, Karthik (University of Southern California) | Ranjith, Rahul (University of Southern California) | Tahir, Sofiane (ADNOC) | Aminzadeh, Fred (University of Southern California) | Yegin, Cengiz (Texas A&M University)
Diatomites are high-porosity, low-permeability reservoirs with elastoplastic properties and high geo-mechanical responsiveness. Despite that, diatomites have great potential for oil recovery. Withdrawal of fluids from the reservoir rock leads to subsidence causing compaction and shear stresses. This disturbed stress distribution results in well failures that causes loss of millions of dollars. Successful maintenance of pressure support through optimum injection/production is key to preventing subsidence to mitigate the risk of well failure and achieve better sweep efficiency for recovery.
There have been different approaches to tackle subsidence and well failures in diatomites including the use of ‘backpressure method’ coupled with a neural network to optimize injection-production to ‘balance’ the rock in terms of stress-distribution and thus decrease well failure due to shearing. However, using such methods may mask other problems the well is experiencing, such as, mechanical issues that influence production. Another existing approach, satellite-imaging (InSAR) cannot be used to take real-time actions that is crucial in diatomites.
Surface tiltmeter data is collected to undertsand the relationship between injection/production and resulting surface deformation, which also provides information about well-to-well connectivity. A neural network-based approach is followed to determine the nonlinear relationship between surface subsidence/dilation and injection-production. This is then used to build an objective function that works to minimize the differences between well-to-well subsidence/dilation measured by the tiltmeters, by adjusting injection-production for the wells.
In this paper, a method that harnesses real-time surface tiltmeter data to adjust injection-production distribution in diatomites to decrease well failures is used beyond the existing applications of surface tiltmeter, such as, in the areas of detection of early steam breach to surface in steam operations and fracture orientation and it provides real-time data for robust reservoir management of such reservoirs where satellite imaging is not effective.
Ezekiel, Justin (China University of Petroleum) | Wang, Yuting (China University of Petroleum) | Liu, Yanmin (China University of Petroleum) | Zhang, Liang (China University of Petroleum) | Deng, Junyu (China University of Petroleum) | Ren, Shaoran (China University of Petroleum)
This paper provides a comprehensive overview on the oxidation reactions and improved oil recovery (IOR) processes of air injection into low permeability light oil reservoir based on detailed analysis of some field projects and reservoir simulation case study carried out on a largely dipping, low permeability light oil reservoir, the Q131 oil block located in Eastern China to analyze the characteristics and processes of air injection. Kinetic models of low temperature oxidation (LTO) reactions were designed and used in the reservoir simulation study to predict oxygen consumption in the reservoir, examine the reaction schemes, IOR mechanisms, and the thermal effect of oxidation reactions occurring during the air injection process. The results of the study including temperature effects, oxygen concentration, oil saturation, gas breakthrough, GOR, and cumulative oil produced were outlined and discussed in details.