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ABSTRACT The industry is facing significant challenges due to the recent downturn in oil prices, particularly for the development of tight reservoirs. It is more critical than ever to 1) identify the sweet spots with less uncertainty and 2) optimize the completion-design parameters. The overall objective of this study is to quantify and compare the effects of reservoir quality and completion intensity on well productivity. We developed a supervised fuzzy clustering (SFC) algorithm to rank reservoir quality and completion intensity, and analyze their relative impacts on wells' productivity. We collected reservoir properties and completion-design parameters of 1,784 horizontal oil and gas wells completed in the Western Canadian Sedimentary Basin. Then, we used SFC to classify 1) reservoir quality represented by porosity, hydrocarbon saturation, net pay thickness and initial reservoir pressure; and 2) completion-design intensity represented by proppant concentration, number of stages and injected water volume per stage. Finally, we investigated the relative impacts of reservoir quality and completion intensity on wells' productivity in terms of first year cumulative barrel of oil equivalent (BOE). The results show that in low-quality reservoirs, wells' productivity follows reservoir quality. However, in high-quality reservoirs, the role of completion-design becomes significant, and the productivity can be deterred by inefficient completion design. The results suggest that in low-quality reservoirs, the productivity can be enhanced with less intense completion design, while in high-quality reservoirs, a more intense completion significantly enhances the productivity. Keywords Reservoir quality; completion intensity; supervised fuzzy clustering, approximate reasoning,tight reservoirs development
Pore-scale images and core-scale imbibition experiments suggest that hydrocarbon and water tend to flow through their own pore network, referred to as "stratified flow". The objective of this paper is to investigate the occurrence of these phenomena at reservoir scale by analyzing flowback and post-flowback production data. Understanding multiphase flow regimes is the first step for selecting proper relative permeability curves and developing representative multiphase rate-transient models. Another objective of this paper is to investigate the harmonic decline (HD) behavior of water-oil ratio (WOR) versus cumulative water production volume and how it could be employed to compare load recovery performance of different wells. We analyzed flowback and post-flowback production data of six multi-fractured horizontal oil wells completed in Eagleford Formation. The proposed data-driven methodology involves using multiphase diagnostic plots of rate-normalized pressure, rate decline, and WOR. We applied this methodology to i) investigate the relationship between water and hydrocarbon at early production time; ii) model WOR with respect to cumulative water production; and iii) evaluate how fracturing/completion design parameters affect well performance.
The results show three key findings: i) During early production time, we observe independent flow regimes (stratified flow) of water and oil indicating their production under different drive mechanisms. Water is produced from an apparently closed tank comprising induced fractures and the surrounding stimulated matrix, and oil is produced independently at a significantly lower rate due to oil influx from matrix into fractures. ii) After jet-pump installation, we observe coupled flow of water and oil indicating their production under similar drive mechanisms provided by the pump. iii) Semi-log plot of WOR versus cumulative water production shows HD trend that is relatively less sensitive to operational changes compared to water rate-decline plots.
Abstract In this paper, we analyze and simulate the production data before and after an extended shut-in period from a horizontal well completed in the Montney Formation. After flowback and early post-flowback production, the well was shut-in for 7 months due to facility completion. When the well was reopened, the hydrocarbon production rates increased significantly compared to the values before the shut-in. To investigate the reasons behind this enhancement, we simulated three-phase production rates and bottom-hole pressure using the actual reservoir geological model. To match the production data before the shut-in period, we had to account for the reduction in oil and gas relative permeabilities due to water blockage. This was done by using multipliers of interblock fluid-flow transmissibility near the matrix-fracture interface. We used these transmissibility multipliers as matching parameters, to achieve the match between measured and simulated production data. However, the best history match was achieved, when the values of transmissibility multipliers are increased by 6.5 times after the shut-in. This suggests a significant increase in oil and gas relative permeabilities due to reduction in water blockage near fracture-matrix interface during the extended shut-in period. Since the simulation model was not able to capture the imbibition process controlled by different driving forces, we used transmissibility multipliers to mimic this phenomenon and its corresponding effects on production rates. In addition, we performed sensitivity analyses to investigate the effects of shut-in on the well productivity and economic profitability in terms of net present value (NPV). The results show that for this well, a 6-month shut-in period is optimal for maximizing NPV and hydrocarbon production.
Mahmoud, Ahmed Abdulhamid (King Fahd University of Petroleum & Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum & Minerals) | Al-AbdulJabbar, Ahmad (King Fahd University of Petroleum & Minerals) | Moussa, Tamer (King Fahd University of Petroleum & Minerals) | Gamal, Hany (King Fahd University of Petroleum & Minerals) | Shehri, Dhafer Al (King Fahd University of Petroleum & Minerals)
ABSTRACT This study introduces an empirical equation for estimation of the rate of penetration (ROP) while horizontally drilling carbonate formations based on the surface measurable drilling parameters, well log data, and the extracted weights and biases of an optimized artificial neural networks (ANN) model. The ANN model was trained using 3000 datasets of different surface measurable drilling parameters including the torque, rotation speed, and weight-on-bit, with the conventional well log data of the deep resistivity, gamma-ray, and formation bulk density, and their corresponding ROP, the self-adaptive differential evolution algorithm was applied to optimize the ANN model's design parameters. For the training dataset, the ROP was predicted with the optimized ANN model with an average absolute percentage error (AAPE) and a correlation coefficient (R) of 5.12% and 0.960, respectively. The developed empirical equation was tested on another unseen dataset (531 data points) collected from the same training well; where it predicted the ROP with AAPE of 5.80% and R of 0.951. 1. INTRODUCTION The total cost of drilling a hydrocarbon well is time-dependent (Lyons and Plisga, 2004). Rig time, which is affected by many factors, such as rate of penetration (ROP), is considered the most critical parameter for determining the total cost of drilling. Optimizing ROP has a significant impact on reducing the total cost (Barbosa et al., 2019). ROP is affected by several parameters, which can be categorized into controllable and uncontrollable parameters (Hossain and Al-Majed, 2015). The controllable parameters include weight-on-bit (WOB), rotation speed (RPM), pumping rate (GPM), torque (T), and standpipe pressure (SPP) (Eren and Ozbayoglu, 2010; Payette et al., 2017). All abbreviations are listed in Appendix A. The uncontrollable parameters include bit size and drilling fluid type, density, and rheological properties. The uncontrollable parameters affect each other, which complicates the quantification of their effect on ROP (Osgouei, 2007).
The mechanical behavior of the rocks can greatly assist in optimizing the drilling operation and well completion design. This behavior can be expressed in terms of Young’s modulus and Poisson’s ratio. Reliable Poisson’s ratio values can be estimated experimentally from core measurements however this method consumes time and economically ineffective.
This study involved the development of two models using neural networks (ANN) and fuzzy logic to estimate static Poisson’s ratio (PRstatic) of sandstone rocks based on the conventional well-log data including bulk density and sonic log data. The models are developed using 692 of actual data core data and the corresponding logging data. The models are optimized after several runs of the different combinations of the available tuning parameters.
The results showed that the neural network model outperformed the model developed using the fuzzy logic tool and yielded a great match with correlation coefficient (R) of 0.98 and AAPE of 1.5% between the predicted and measured PRstatic values. The developed ANN-based model is then validated using unseen data from another well within the field to estimate PRstatic over a certain interval. The validation process results showed a significant agreement with correlation coefficient (R) of 0.95 between the predicted PRstatic values and the actual measured ones. The results demonstrated the ability of the developed model to provide a continuous profile of static Poisson’s ratio (PRstatic) whenever the petrophysical logging data are available.
Abstract Alaska North Slope (ANS) contains heavy oil (12-23 API) with an estimated range of original oil in place (OOIP) of 13 to 25 billion barrels. This resource has been considered as one of the largest heavy oil deposits in the United States. However, several technical challenges are limiting its commercial development. One of the most significant difficulty is the overlying 1,800-2,000 ft-thick permafrost layer, which causes unavoidable significant heat losses when steam is injected from the surface steam generators, and the potentially disastrous effects due to melting of the permafrost around the cemented casing. Therefore, surface steam injection might not be a viable option to develop this resource, although it is the most commonly used approach of heavy-oil recovery. Hence, the objective of this research is to conduct a feasibility study on the application of new approach, in which steam is generated downhole using thermochemical reaction (SGT) combined with steam assisted gravity drainage (SAGD), to recover heavy oil from Ugnu reservoir which is one of the major oil deposits in ANS. A numerical simulation model for Ugnu heavy oil reservoir is built using CMG-STARS simulator. Then a MATLAB framework is integrated with the simulation model to study different recovery strategies on the project profitability. Net Present Value (NPV) is used in this study as the key performance indicator while monitoring oil recovery factor (RF) and cumulative oil production (COP). The design and operational parameters studied and optimized in this paper involve; 1) well configurations and locations, 2) steam injection rate and quality, steam trap and startup heating period to initialize SAGD process. The results show that the in-situ SGT is a successful approach to recover heavy oil from Ugnu reservoir and it yields high project profitability, in terms of the NPV after 10 years of development. The main reason of this outperformance is ability of SGT to avoid the significant heat losses and associated costs, in the surface steam injection methods. This paper introduces a novel approach to generate downhole steam using thermochemical reactions to overcome the challenges associated with heavy-oil resource development from Ugnu reservoir, while minimizing the environmental impact by reducing the greenhouse gas emission related to surface steam injection methods.
Abstract Hydraulic fracturing combined with horizontal drilling is the key to unlocking vast unconventional reservoirs. However, understanding the relationship between fracturing/completion-design parameters and the process efficiency remains challenging. The objectives of this paper are 1) to estimate initial fracture volume and its variations during the production by using flowback data and 2) to investigate the existence of correlations between completion-design parameters and induced fracture volume process optimization. We analyze flowback data and completion-design parameters of 16 shale-gas completed in the Eagle Ford Formation. First, we estimate ultimate water recovery and initial fracture volume by using harmonic-decline model, and fracture volume loss during flowback by using a new iterative approach that accounts for fracture-porosity changes with time. Then, we conduct a multivariate analysis to develop empirical correlations of completions-design parameters with initial fracture volume and fracture characteristic-closure rate (FCR). The results show that harmonic-decline model could be used to estimate initial fracture volume with an average absolute percentage error (AAPE) of 7%. The correlations developed between initial fracture volume and completion-design parameters show that the proppant concentration has the most significant effect on fracture volume, followed by gross perforated interval (GPI) and shut-in time, respectively. Total vertical depth (TVD) and fluid injection rate have insignificant effects. The results indicate that increasing choke size during early flowback leads to a relatively-sharp decrease in fracture volume, while changing choke size during late flowback has negligible effects. The proposed correlation between FCR and completion-design parameters demonstrates the significant effect of proppant concentration on fracture closure during flowback, while GPI and TVD have negligible effects.
Moussa, Tamer (King Fahd University of Petroleum and Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum and Minerals) | Patil, Shirish (King Fahd University of Petroleum and Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum and Minerals) | Abdelgawad, Khaled (King Fahd University of Petroleum and Minerals)
Abstract Thermal recovery methods are viable and commonly used to recover heavy oil reservoirs by reducing oil viscosity and improving oil displacement. However, there are many challenges associated with conventional steam injection methods. These challenges include the significant heat energy losses before steam reaches the reservoir, the high cost of steam generation and injection, as well as the emission of greenhouse gases. Therefore, it is essential to introduce a heavy oil recovery approach in which steam can be generated downhole to overcome these challenges associated with conventional steam injection methods. However, this novel heavy-oil recovery method has several designs and operational parameters that must be efficiently optimized, to achieve maximum recovery from heavy-oil reservoirs with less cost and minimum environmental impact. The objective of this work is to introduce a novel heavy-oil recovery technique using in-situ steam generated by downhole thermochemical reactions and investigate the key design and operational parameters of this complex recovery process. Modified self-adaptive differential evolution (MSaDE) and particle swarm optimization (PSO) methods are used in this work as global optimizer to find the optimum design and operation parameters to achieve the maximum net present value (NPV) and highest oil recovery (RF) of a heavy-oil reservoir after ten years of development. Comparison of the two proposed optimization methods is introduced as well. The results show that downhole thermochemical reactions can be used to generate in-situ steam, to efficiently reduce the heavy-oil viscosity and improve oil mobility. It has also shown that utilizing MSaDE and PSO methods to optimize the key components of this novel recovery process, significantly enhanced the recovery performance, in terms of higher NPV and RF. This study provides the first known in-depth optimization and uncertainty analysis to outline the significance of each design and operation parameter of the proposed novel thermochemical recovery process. This work showed and verified the concept of using downhole thermochemical reactions as an environmental-friendly solution to recover oil from heavy-oil reservoirs and is considered as a step forward to eliminate the greenhouse gases emission related to thermal recovery methods.
Moussa, Tamer (King Fahd University of Petroleum and Minerals) | Patil, Shirish (King Fahd University of Petroleum and Minerals) | Mahmoud, Mohamed (King Fahd University of Petroleum and Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum and Minerals)
Abstract Determination of optimal well locations plays an important role in the efficient recovery of hydrocarbon resources. However, it is a challenging and complex task because it relies on reservoir, and fluid and economic variables that are often nonlinearly correlated. Traditionally, well placement optimization (WPO) has been done through experience and use of quality maps. However, reservoir management teams are beginning to appreciate the use of automatic optimization tools for well placement that will yield the largest financial returns or highest net present value (NPV). In addition, the performance of a reservoir is time and process dependent, therefore well placement decisions cannot be based on static properties alone. On the other hand, well placement optimization requires a large number of simulator runs in an iterative process, and thus several runs to reach the maximum achievable NPV. Therefore, there is a real need for automatic well placement approach that uses highly efficient optimization method, which can improve the result quality, speed of the convergence process to optimal result and thus decrease the time required for computation. The objective of this work is to determine the optimal well locations in a heavy oil reservoir under production using a novel recovery process, in which steam is generated, in-situ, using thermochemical reactions. Self-adaptive differential evolution (SaDE) and particle swarm optimization (PSO) methods are used as the global optimizer to find the optimal configuration of wells that will yield the highest NPV. Comparison analysis between the two proposed optimization techniques is introduced. The CMG STARS Simulator is utilized in this research to simulate reservoir models with different well configurations. Comparison of results is made between the NPV achieved by the well configuration proposed by the SaDE and PSO methods. The results show that SaDE performed better than PSO in terms of higher NPV after ten years of production while under in-situ steam injection process using thermochemical reactions. This is the first known application where SaDE and PSO methods are used to optimize well locations in a heavy oil reservoir that is recovered by injecting steam generated in-situ using thermo-chemical reactions. This research shows the importance of well placement optimization in a highly promising and novel heavy oil recovery process. This also is a step forward in the direction to eliminate the CO2 emissions related to thermal recovery processes.
Abstract Heavy oil has attracted global attention due to its huge volume of original oil in place. However, there are numerous operation and economic challenges to the recovery of heavy oil due to its high-viscosity and high-density. Thermal recovery methods such as steam injection is viable and commonly used to recover heavy oil and bitumen primarily by viscosity reduction of heavy oil and improving the displacement of the heavy oil. However, there are significant heat losses before the steam reaches the heavy-oil reservoir, in addition to the concerns of high cost and emission of greenhouse gases. One of the promising new heavy-oil recovery approaches is generating steam with nitrogen gas, in-situ, using thermochemical reaction to reduce oil viscosity, improve the mobility ratio and enhance the heavy-oil displacement. Steam and nitrogen are generated, in-situ, by injecting exothermic reactants downhole with the injected water to create heat and enhance reservoir pressure for mobilizing heavy oil. The exothermic reaction is triggered by either increasing downhole temperature or in the presence of a low pH weak acid. In this research, a numerical study of the novel heavy oil recovery process using in-situ steam and nitrogen generated by thermochemical reactions in field scale is conducted. Various ratios of nitrogen- steam are studied to identify their effect on the recovery efficiency. In-situ Nitrogen-steam ratio generated by thermochemical is optimized to accomplish the maximum achievable net present value (NPV) after ten years of recovery. The CMG STARS simulator is used to simulate reservoir models with different operational parameters. The results show that the generated heat from in-situ thermochemical reactions was sufficient to reduce the viscosity of heavy oil, while the generated nitrogen gas provided a good heat insulation effect and reduced steam-oil ratio. Thus, higher NPV was achieved than typical conventional steam-only injection method. This is the first time NPV and all economic parameters are considered to analyze the performance of an in-situ steam and nitrogen generated by thermochemical reaction. This research shows that the recovery of the proposed method is more suitable and economical for the reservoirs which are not viable for conventional steam flooding methods and it is a step forward to eliminate CO2 emissions associated with thermal recovery process.