Al-Hameedi, Abo Taleb T. (Missouri University of Science and Technology) | Alkinani, Husam H. (Missouri University of Science and Technology) | Dunn-Norman, Shari (Missouri University of Science and Technology) | Alashwak, Nawaf A. (Missouri University of Science and Technology) | Alshammari, Abdullah F. (Missouri University of Science and Technology) | Alkhamis, Mohammed M. (Missouri University of Science and Technology) | Mutar, Rusul A. (Ministry of Communications and Technology)
Drilling wastes generated in large volumes is recognized to have many effects on the environment. Several techniques have been applied by the oil and gas industry to overcome the impacts of drilling waste on the environment, an example of these techniques is using environmental friendly drilling fluid additives.
This work investigates the potential of using White Sunflower Seeds’ Shell Powder (WSSSP) as an environmental friendly drilling fluid additive. This material was prepared in-house. Experimental evaluation has been carried out to investigate the ability of WSSSP to enhance several properties of water-based drilling fluid under two different pH conditions. The WSSSP was first evaluated at 9.3 pH then the pH was increased using sodium hydroxide to 11.5. Several properties of drilling fluid were measured. The measurements included testing the rheological properties using viscometer, measuring the filtration using standard low-pressure low-temperature filter press, the pH using pH tester, and other important properties.
The findings of this work showed that WSSSP in 9.3 pH environment reduced the fluid loss by 18% and 30% when 1% and 2% concentrations of WSSSP were added, respectively. This reduction in fluid loss was along with forming a thin filter cake. The filter cake thickness of the reference fluid was decreased from 3 mm to 2.14 mm and 1.9 mm at 1% and 2% concentrations of WSSSP. Additionally, WSSSP resulted in increasing the plastic viscosity (PV) compared to the reference fluid by 33.33% at 1% and 2% concentrations. While the yield point (YP) was increased by 22.22% and 44.44% when 1% and 2% concentrations of WSSSP were added, respectively. Both the initial and final gel strengths were increased by 27.27%, 44.44 %, 7.14% and 14.28% at 1% and 2% concentrations, respectively. Moreover, the results in 11.5 pH emphasized the efficient performance of WSSSP, and it showed better improvement in the filtration specifications and the rheological properties. In other words, PV, YP, and gel strength were significantly increased; while the fluid loss was very low and the filter cake was very thin at 11.5 pH condition compared to 9.3 pH condition for the same concentrations, proving the ability of WSSSP to perform better under higher pH condition.
The significant enhancement in the rheological and filtration properties, suggesting the applicability of using this additive as a rheology modifier and filtration control agent. These results showed the potential use of WSSSP as an alternative for some of the toxic materials used today in the oil and gas industry. This work demonstrates that this additive will help to reduce both the impact on the environment along with reducing the cost of drilling fluid and drilling waste handling.
SPE is educating the next generation of aspiring engineers, scientists and managers about the oil and gas industry. This is an opportunity for school students in grades 9–12, studying Mathematics, Physics, Chemistry, Geography or interested in Petroleum Engineering are invited to join SPE members from all over the globe to discover the world of Petroleum Engineering. School teachers are invited to bring a group of 10–15 students. Students will be treated to a range of hands-on activities and presentations from renowned engineers. The oil price outlook coupled with the response of each oil and gas company to make ends meet has led to severe exploration budget cuts.
The operator piloted a new well-completion design combining inflow-control valves (ICVs) in the shallow reservoir and inflow-control devices (ICDs) in the deeper reservoir, both deployed in a water-injector well for the first time in the company’s experience. In this paper, the authors describe a project to design, field trial, and qualify an alternative solution for real-time monitoring of the oil rim in carbonate reservoirs that overcomes these disadvantages. The authors detail the development of a technique based on surface-to-borehole controlled-source electromagnetics (CSEM), which exploits the large contrast in resistivity between injected water and oil to derive 3D resistivity distributions, proportional to saturations, in the reservoir. This industry is one often considered reactive and overly tradition-bound. These new technologies, however—and, more importantly, the drive of these researchers to harness their capabilities—prove that petroleum engineers remain at the forefront of innovation and discovery.
After a long cooling off period, this dry-gas shale play is once again red hot. Ghawar vs. Permian Basin: Is There Even a Comparison? While some try to put the two enormous oil producers toe-to-toe, the best thing to do might be to understand why they are different. Machine-learning methods have gained tremendous attention in the last decade. The underlying idea behind machine learning is that computers can identify patterns and learn from data with minimal human intervention.
The basic objective of this course is to introduce the overview and concept of production optimisation, using nodal analysis as a tool in production optimisation and enhancement. The participants are exposed to the analysis of various elements that help in production system starting from reservoir to surface processing facilities and their effect on the performance of the total production system. Depth conversion of time interpretations is a basic skill set for interpreters. There is no single methodology that is optimal for all cases. Next, appropriate depth methods will be presented. Depth imaging should be considered an integral component of interpretation. If the results derived from depth imaging are intended to mitigate risk, the interpreter must actively guide the process.
Fiallos Torres, Mauricio Xavier (The University of Texas at Austin) | Yu, Wei (The University of Texas at Austin) | Ganjdanesh, Reza (The University of Texas at Austin) | Kerr, Erich (EP Energy) | Sepehrnoori, Kamy (The University of Texas at Austin) | Miao, Jijun (SimTech LLC) | Ambrose, Raymond (EP Energy)
Optimizing spacing of infill wells and fractures can lead to large rewards for shale field operators, and these considerations have influences on primary and tertiary development of the field. Although several studies have been employed to show the existence of well interference, few models have also implemented Huff-n-Puff and injection containment methods to optimize further hydraulic fracture designs and pressure containment to improve the efficiency of Enhanced Oil Recovery (EOR). This study has performed a rigorous workflow for estimating the impacts of spatial variations in fracture conductivity and complexity on fracture geometries of interwell interference. Furthermore, we applied a non-intrusive embedded discrete fracture model (EDFM) method in conjunction with a commercial compositional reservoir simulator to investigate the impact of well interference through connecting fractures by multi-well history matching to propose profitable opportunities for Huff-n-Puff application. First, based on a robust understanding of fracture properties, updated production data and multi-pad wellbore image logging data from Eagle Ford, the model was constructed to perform nine wells sector model history matching. Later, inter-well connecting fractures were employed for enhanced history matching where results varied significantly from unmeasured fracture sensitivities. The result is the implementation of Huff-n-Puff models that capture inter-well interference seen in the field and their affordable impact sensitivities focused on variable injection rates/locations and multi-point water injection to mimic pressure barriers. The simulation results strengthened the understanding of modeling complex fracture geometries with robust history matching and support the need to incorporate containment strategies. Moreover, the simulation outcomes show that well interference is present and reduces effectiveness of the fracture hits when connecting natural fractures. As a result of the inter-well long fractures, the bottom hole pressure behavior of the parent wells tends to equalize, and the pressure does not recover fast enough. Furthermore, the EDFM application is strongly supported by complex fracture propagation interpretation and ductility to be represented in the reservoir. Through this study, multiple containment scenarios were proposed to contain the pressure in the area of interest.
The model has become a valuable template to inform the impacts on well location and spacing, completion design, initial huff-n-puff decisions, subsequent containment strategies (e.g. to improve cycle timing and efficiency), and to expand to other areas of the field. The simulation results and understandings afforded have been applied to the field satisfactorily to support pressure containment benefits that lead to increased pressure build, reduced gas communication, reduced offset shut-in volumes, and ultimately, improvements in net utilization and capital efficiency.
Rosenhagen, Nicolas M. (Colorado School of Mines) | Nash, Steven D. (Anadarko Petroleum Corporation) | Dobbs, Walter C. (Anadarko Petroleum Corporation) | Tanner, Kevin V. (Anadarko Petroleum Corporation)
The volume of stimulation fluid injected during hydraulic fracturing is a key performance driver in the horizontal development of the Niobrara formation in the Denver-Julesburg (DJ) Basin, Colorado. Oil production per well generally increases with stimulation fluid volume. Often, operators normalize both production and fluid volume based on stimulated lateral length and investigate relationships using "per-ft" variables. However, data from well-based approaches commonly display such wide distributions that no useful relationships can be inferred. To improve data correlations, multivariate analysis normalizes for parameters such as thermal maturity, depth, depletion, proppant intensity, drawdown, geology and completion design. Although advancements in computing power have decreased cycle times for multivariate analysis, preparing a clean dataset for thousands of wells remains challenging. A proposed analytical method using publicly available data allows interpreters to see through the noise and find informative correlations.
Using a data set of over 5000 wells, we aggregate cumulative oil production and stimulation fluid volumes to a per-section basis then normalize by hydrocarbon pore volume (HCPV) per section. Dimensionless section-level Cumulative Oil versus Stimulation Fluid Plots ("Normalization" or "N-Plot") present data distributions sufficiently well-defined to provide an interpretation and design basis of well spacing and stimulation fluid volumes for multi-well development. When coupled with geologic characterization, the trends guide further refinement of development optimization and well performance predictions.
Two example applications using the N-Plot are introduced. The first involves construction of predictive production models and associated evaluation of alternative development scenarios with different combinations of well spacing and completion fluid intensity. The second involves "just-in-time" modification of fluid intensity for drilled but uncompleted wells (DUC's) to optimize cost-forward project economics in an evolving commodity price environment.
Fiallos, Mauricio Xavier (The University of Texas at Austin) | Yu, Wei (The University of Texas at Austin) | Ganjdanesh, Reza (The University of Texas at Austin) | Kerr, Erich (EP Energy) | Sepehrnoori, Kamy (The University of Texas at Austin) | Miao, Jijun (SimTech LLC) | Ambrose, Raymond (EP Energy)
Shale field operators have vested interest in optimal spacing of infill wells and further fracture optimization, which ideally should have as little interference with the existing wells as possible. Although proper modeling has been employed to show the existence of well interference, few models have forecasted the impact of multiple inter-well fractures on child wells production to optimize further hydraulic fracture designs. This study presented a rigorous workflow for estimating the impacts of spatial variations in fracture conductivity and complexity on fracture geometries of inter-well interference. Furthermore, we applied a non-intrusive embedded discrete fracture model (EDFM) method in conjunction with a commercial black oil reservoir simulator to investigate the impact of well interference through connecting fractures by multi-well history matching, based on a robust understanding of fracture properties, real production data and wellbore image logging. First, according to updated production data from Eagle Ford, the model was constructed to perform four (parent) wells history matching including five inner (child) wells. Later, fracture diagnostic results from well image logging were employed to perform sensitivity analysis on properties of long interwell connecting fractures such as number, conductivity, geometry, and explore their impacts on history matching. Finally, optimal cluster spacing was recommended considering interwell interference. The simulation results show that well interference is present and reduces effectiveness of the fracture hits when the connecting fracture conductivity, primary fracture conductivity, and number of connecting fractures increase. Because of these interwell long fractures, the bottomhole pressure behavior of the parent wells tends to equalize. Furthermore, the EDFM application is strongly supported by complex fracture propagation interpretation from image logs through the child wells in the reservoir. Through this study, three possible scenarios are shown with robust history matching of the model considering more than 20 complex dominant long interwell fracture hits and more than 2000 hydraulic fractures.
The model became a valuable stencil to decide the well location and spacing, the completion staging, and to optimize the hydraulic fracture treatment design as well as its sequence so that it can be expanded to other areas of the field. The simulation results were applied to the field successfully to afford significant reductions in offset frac interference by up to 50% and reduce completion costs up to 23% while improving new well capital efficiency.
Recent casing failures in the Granite Wash play in the western Anadarko Basin have sparked deep concerns to operators in North Texas and Oklahoma. Hydrostatic tests made in the field show that present API standards do not assure adequate joint and bursting strength to meet deep-well requirements. Past and present literature has been infested with numerous casing failures incidents. Despite the extensive documentation and recommendations, a mounting trend of failure is still on the rise. In an attempt to find possible solutions for these failures, this study is a continuation of an ongoing effort to minimize the likelihood of failure using Data Mining and Machine Learning (ML) algorithms. The study applied both descriptive visual representations such as Mosaic and Box Plots and predictive algorithms including Artificial Neural Networks (ANN) and Boosted Ensemble trees on eighty land-based wells, of which twenty possessed casing and tubing failures. The study used a predictive analytics software and python coding to evaluate twenty-six different features compiled from drilling, fracturing, and geologic data. This work attempts to shed light on operational problems and implement a Data Analytic approach to find out the possible factors contributing to casing failures using both descriptive and supervised ML algorithms.