In this paper we describe a novel method for water unloading of natural gas wells in mature reservoirs experiencing low reservoir pressures. Current methods for water unloading from gas wells have at least one of the drawbacks of restricting gas production, requiring external energy, using consumable surfactants, or being labor intensive. The proposed design offers a new approach to water unloading that does not restrict or interrupt gas production. It can operate without external energy, and uses no consumables. Virtual and physical simulators have been developed and the full-scale version of the concept has been studied in test wells to demonstrate the feasibility and performance of the new water-unloading concept. An industrial-grade preproduction prototype was tested successfully in a test gas well to validate this study.
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.
In this paper we discuss our studies conducted on two California offshore fields that may be abandoned in near future. The purpose of the study was to examine the feasibility of re-purposing these fields to suitable offshore gas storage by utilizing the reservoir voidage and by using the existing pipeline facilities. These storage sites could offer a significant alternative to the current onshore sites located in highly populated urban areas of California. Gas storage in certain California offshore fields producing from the fractured Monterey formation could eliminate the potential environmental risks associated with urban onshore storage of gas and prevent incidents such as the October 23, 2015 one in the Aliso Canyon Natural Gas Storage Facility in Los Angeles County. The 100,000 tonnes of methane emitted into the atmosphere resulted in the relocation of thousands of people from the areas proximal to the facility. Study of the caprock and initial reservoir pressures encountered in these fields shows that a proposed 3000 psi storage pressure is safe for offshore storage purposes. Our computation of voidage caused by more than 3 decades of production shows that on a total basis, under a storage pressure cap of 3000 psi, these two fields together can help in storage of more than 3 TCF of gas. This is about 5 times the existing storage capacity of 0.6 TCF in California. On the long terms basis, the proposed offshore storage fields could provide a secure source of energy for the evolving market of California CNG based transportation, power generation and other consumer needs.
In this paper, we present an Artificial Neural Network (ANN) solution approach to estimate average reservoir pressure from partial data recorded from fall-off tests. The methodology, while relatively simple, is entirely based on calibrating data from well tests with full fall-off information. Compared with conventional reservoir pressure estimation models, which usually require a long shut-in time and manual analysis for an individual well, this approach provides an efficient way of processing large data sets. Using data from injection well fall-off tests can avoid cost from non-producing periods of production wells. Also, utilizing injection profile tracers with shut-in pressure trends at the surface, we can infer the sand face pressure trends assuming a hydrostatic column during a shut-in period, greatly reducing instrumentation costs and downhole jewelry requirements.
This methodology is extremely helpful for mapping average pressure around injection wells in a reservoir with hundreds of injection wells with multiple flow units. In principle, maps generated from fall-off tests can be used as diagnostic tools to assist in formulating water flooding plans, ameliorating oil production capability, arranging well pattern density and preserving reservoir stability by providing an overview of internal oilfield conditions. Because of its practical simplicity and maneuverability, this new methodology can rapidly offer actionable data and generate remarkable business value.
We studied the applicability of Step Rate Tests (SRTs) for estimation of suitable injection gradients and reviewed various diagnostic plots to examine their relationship with injection rate and pressure. While dealing with multi-layered systems and those consisting of unconsolidated formations and examining the causes of injection increase seen, we identified some shortfalls associated with the SRT procedure. We also examined the force balance of formation response to injection gradients and concluded that formation resistance to water injection changes with time. That means the injection gradient to maintain constant injectivity is affected by the near wellbore conditions and as such, has a dynamic nature and can change with time. For unconsolidated formations, changes in pore pressure may also affect the rock effective permeability which alone can influence the required injection gradients.
Zhao, Xiaoxi (University of Southern California) | Popa, Andrei (Chevron Corporation) | Ershaghi, Iraj (University of Southern California) | Aminzadeh, Fred (University of Southern California) | Casidy, Steve (Chevron Corporation)
Permeability is one of the most important parameters in reservoir characterization. Core measurements are usually used to provide this information. However, core data can be limited in certain locationsand is expensive to collect. Various methodologies have been used in the industry to predict permeability through porosity or other information with limited success. In this paper, a workflow has been proposed to first identify the relationship between well logging and core permeability data. Using various data mining methods, data preprocessing and filtration are conducted to detect outliers and to ensure the success of permeability prediction. A case study of a heterogenous reservoir has been conducted with this proposed method. Preprocessing results were compared with additional geological and geophysical information in order to understand data behavior. An artificial neural network was applied to train the model by using well logging information as the input and permeability values as the output. Different data normalization methods, input data combination, training function and different neural network architecturesweretested in order to get better performance with reasonable time consumption. Prediction results showed consistency in predicted verses true permeability. The study showed that, for a rather heterogeneous reservoir, Artificial Intelligence can provide consistent permeability prediction with well logging data which can help in the decision-making process.
It should be noted that this study is not unique, similar work has been conducted and is available in the literature. What is noteworthy about this study is that this method provided a significantly improved permeability estimation compared to the classical regression models available in modeling software applications such as Geolog
In today's exponentially changing technological landscape, the upgrading of skills and knowledge is of critical importance. Like in other professional fields, engineers, working in the oil and gas industry also need to pursue advanced degrees and to keep their skills up to date. For anyone working in the petroleum industry, partly because of the remote location of operations and in part because of frequent travel-associated assignments, the environment for pursuing advanced degrees may seem restrictive and impossible. Also, the nature of the oil and gas industry often requires professionals to work in locations away from major cities; often travel to remote locations and be away from their offices for a substantial period. This can be discouraging and make it impossible for these professionals to enroll in traditional on-campus educational degree programs. More importantly, even if located near a major metropolitan area, there may not be a credible or relevant local university program to provide the necessary education nor is there expectation to teach such graduate courses. Indeed, the desire to enhance professional qualifications and to acquire graduate education can rapidly diminish when there is limited access to the accredited educational providers. Added to that is a fear for getting involved but later facing roadblock in balancing work, family responsibilities and the intended educational program.
In recent years, an effective way to circumvent this obstacle has been relying on modern communication methods that enable access to accredited advanced education via high-quality Internet to the classroom regardless of individual's location. This breakthrough has provided a unique continuous learning opportunity for many engineering or science graduates in oil and gas professions and at various stages of their careers. But experience shows that the course delivery besides video communication needs other components to provide an inviting, effective and appealing virtual learning environment. We share experiences gained at the DEN@Viterbi system that has included many conveniences to the part time students
In this paper, the focus is on the changes happening in the oil industry and the evolution of new tools and technologies transferred from other fields aiming to make the industry, more efficient and more acceptable to the consumer base. The discussion about the changing trends in petroleum engineering education is primarily focused on the U. S. producers. But some of the observations may equally apply to other national oil producers.
Currently and for the foreseeable future, oil and gas will continue to be the world's primary energy feedstock. This means that petroleum assets are as critical as they have been during the last century to meet the energy demands globally. If the industry continues its mission in providing oil and natural gas resources to the world communities, it will need the services of appropriately skilled professionals. Oil industry hires many technical professionals. At the core, however, there are unique petroleum engineering concepts and domain knowledge that defines the industry and its technology base. It probably fits better in describing the sciences and techniques as a broader base of subsurface engineering
The oil industry is changing, and parallel to that, are the expanded core competencies needed to take new directions. Some major emphasis areas affecting the shape of petroleum engineering education include technology transfer from the fields of information sciences, medical imaging, and human factor engineering. These all influence the core educational preparation of the petroleum engineering professionals. We discuss the history of PTE (petroleum engineering) education and the evolution of the industry and its manpower needs. As an example, we consider the petroleum engineering program at USC and review how, in response to the changing trends in the oil and gas operations, engagement with the industry has been helpful in opening new directions and changes in the academic educational content offered to the graduates. Utilizing case studies and experiences from the USC program, we illustrate the larger trends which have wider application to the industry and to changing the shape of university petroleum education programs.
Popa, Andrei S. (Chevron Corporations) | O'Toole, Conor (University of Southern California) | Munoz, Juan (Chevron Corporation) | Cassidy, Steve (Chevron Corporation) | Tubbs, Dallas (Chevron Corporation) | Ershaghi, Iraj (University of Southern California)
The successful waterflooding field development depends not only of the understanding of reservoir characterization, sub-surface injection displacement and sweep-efficiency, but also an accurate and effective design and operation of the surface network’s water injection distribution. In certain cases, the latter is critical for the successful waterflood operations in large fields with thousands of wells and high volume of new development activity.
For the purpose of this study, we are presenting a new data-driven approach to accurately estimate the injection rate in all non-instrumented wells in a large waterflooding operation. A collection of data driven tools, including statistics, clustering, simulation and an artificial neural network model were employed to prime and model the data. As a final point, the neural network leverages instrumented wells’ data and serves as an accurate real-time proxy to estimate missing injection rate measurements in non-instrumented wells. The system’s accuracy was validated by comparing the estimated rates for different wells on a different branch with the ones measured at physical wells. The neural network model trained on the cleansed data set revealed a high performance system with a >0.93 R2 values for both training and validation sets.
The paper outlines both the methodology and procedures used to analyze a branch of the water network system, and the modeling of accurate estimation of injection rates. The model performance is remarkable having used only field and wellhead measured data and considering the natural uncertainty inherited in these values. Finally, this system provides the capability to estimate the flow rate for every non-instrumented well in the field and respond to exceptions in relevant time.
Qi, Qianru (University of Southern California) | Pepin, Sophie (University of Southern California) | AlJazzaf, Abdula (University of Southern California) | Ershaghi, Iraj (University of Southern California)
It is common to use the slope of Hall plot as a tool for monitoring changes in well injectivity. Loss of injectivity at a given well head pressure may relate to a build-up of reservoir pressure or a gradual build-up of skin. In this study, we focus on injectivity losses caused by the wellbore skin development. Based on actual field data, we had noted errors in detection of permeability losses when later profile surveys were consulted. Our objective of the study was to examine the problem and then introduce an improved analytical formulation for layered systems and assess the limitations and meaning of slope changes observed on the Hall plot for stacked reservoirs. For calibration purposes, we used both an extended analytical model and a simulation approach to demonstrate the weakness of the Hall Plot for multi-layered cases. In simulating the cases, we used a cumulative injection dependent function to model the permeability losses for a given layer in a stacked system, and examined the limitation of estimated slopes in regarding to indication of the location and extent of the damaged zones in a stacked reservoir for remediation measures. We included a case study to exemplify the situation.