This course discusses the fundamental sand control considerations involved in completing a well and introduces the various sand control techniques commonly used across the industry, including standalone screens, gravel packs, high rate water packs and frac-packs. It requires only a basic understanding of oilfield operations and is intended for drilling, completion and production personnel with some sand control experience who are looking to gain a better understanding of each technique’s advantages, limitations and application window for use in their upcoming completions.
This seminar will teach participants how to identify, evaluate, and quantify risk and uncertainty in everyday oil and gas economic situations. It reviews the development of pragmatic tools, methods, and understandings for professionals that are applicable to companies of all sizes. The seminar also briefly reviews statistics, the relationship between risk and return, and hedging and future markets. Strategic thinking and planning are key elements in an organisation’s journey to maximise value to shareholders, customers, and employees. Through this workshop, attendees will go through the different processes involved in strategic planning including the elements of organisational SWOT, business scenario and options development, elaboration of strategic options and communication to stakeholders.
Green fields today mostly can be regarded as marginal fields and successfully developed. It covers the complete assessment of the oil and gas recovery potential from reservoir structure and formation evaluation, oil and gas reserve mapping, their uncertainties and risks management, feasible reservoir fluid depletion approaches, and to the construction of integrated production systems for cost effective development of the green fields. 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.
PETRONAS FLNG SATU (PFLNG1) is a floating liquefied natural gas facility producing 1.2 million tonnes per annum (mtpa) of LNG, on a facility that is 365m long, and 60m wide, making it among the largest offshore facility ever built. The PFLNG1 project is the first of its kind in the world and is the first deployment of PETRONASâ€™ Floating Liquefied Natural Gas (FLNG) technology, consolidating the traditional offshore to onshore LNG infrastructure into a single facility. This will see a giant floating facility capable of extracting, liquefying and storing LNG at sea, before it is exported to customers around the globe. The FLNG journey has come a long way since 2006, with many technological options explored to monetise and unlock the potential of small and stranded gas fields. Moving an LNG production to an offshore setting poses a demanding set of challenges â€“ as every element of a conventional LNG facility needs to fit into an area roughly one quarter the size in the open seas whilst maintaining safety and increased flexibility to LNG production and delivery. The keynote address describes the breakthrough features of PFLNG1 â€“ the worldâ€™s first floating LNG facility; and the pioneering innovation that it brings to the LNG industry.
Decisions in E&P ventures are affected by Bias, Blindness, and Illusions (BBI) which permeate our analyses, interpretations and decisions. This one-day course examines the influence of these cognitive pitfalls and presents techniques that can be used to mitigate their impact. Bias refers to errors in thinking whereby interpretations and judgments are drawn in an illogical fashion. Blindness is the condition where we fail to see an unexpected event in plain sight. Illusions refer to misleading beliefs based on a false impression of reality.
SPE, through its Energy4me programme, will present a free one-day energy education workshop for science teachers (grades 8–12). A variety of free instructional materials will be available to take back to the classroom. Educators will receive comprehensive, objective information about the scientific concepts of energy and its importance while discovering the world of oil and natural gas exploration and production. Energy4me is an energy educational public outreach programme that highlights how energy works in our everyday lives and promote information about career opportunities in petroleum engineering and the upstream professions. SPE’s Energy4me programme values the role teachers and energy professionals play in educating young people about the importance of energy.
In most US unconventional resources development, operators usually first drill the parent wells to hold their leases, and then infill wells are drilled. A challenge raised from this process is the well-to-well interference or frac-hits. Fractures in infill wells have a tendency to propagate toward the depleted region induced by the pressure sink of the parent well, resulting in asymmetric fracture growth in infill wells and frac-hit with the parent well. One of the available mitigation methods is to inject water into the parent well to re-pressurize the depleted region. Though several papers have released positive results from their numerical studies, both negative and positive responses are reported from filed applications. This paper focused on identifying the mechanism and key factors controlling the effectiveness of the subsequent parent well water injection. A coupling reservoir geomechanical model was built to evaluate the pressure and stress change caused by the parent well production and subsequent parent well water injection. The reservoir and geomechanical models are prepared based on a dataset from Eagle Ford Shale. At desired time steps, pressure distribution from reservoir simulation is used to calculate the corresponding stress status.
In this numerical simulation study, both reservoir properties and operating conditions are considered. Considering the production loss during the parent well injection, the maximum injection time is set to be 1 month. The magnitude and orientation of horizontal principal stresses within and around the depleted region are used as a criterion to evaluate the effectiveness of subsequent parent well injection. A general observation is that between two adjacent fracture clusters, 3 regions could be identified whose behaviors are significantly different during production and injection. The subsequent water injection could only restore the pressure and stress in region 1, which is within 10 ft to the fractures. Region 2 is severely depleted but the injection of 1 month generates no improvement in this region due to the low matrix permeability. Region 3 might exist, where oil is not produced, but Shmin reduces and this reduction could not be restored through injection of 1 month. If the injection generates a relatively uniform pressure distribution, then SHmax angle change could be reduced to 0. We also observed that: (1) for our case, an injection pressure equal to the initial reservoir pressure is recommended. Using low injection pressure, Shmin is found out to be lowest in fractures, which may make infill well fractures tend to propagate into and hit the parent well fractures. However, if injection pressure is increased to larger than the initial reservoir pressure and smaller than the minimum horizontal stress, the improvement is insignificant; (2) Comparison between uniform and non-uniform hydraulic fracture geometries shows that hydraulic fracture geometry mainly affects the depletion region far away from the wellbore. i.e. along the long fracture tips. After injection, in the case with long uniform fractures, the Shmin value in long fracture tips is still lowest. (3) An SRV with high permeability significantly extends the depletion region. If the permeability is not large enough i.e. 0.01 mD, after injection of 1 month, the restored Shmin is about 1000 psi lower than the base case without SRV. (4) Using low bottomhole pressure in production, restored pressure and stress are about 500 psi lower than the base case; and due to the large pressure contrast between region 1 and region 2, the SHmax angle change could not be reduced. (5) In a reservoir with normal pressure, as the pressure change is not large, it is easier for the subsequent injection to take effect.
This paper provides significant insights into how to design a successful subsequent water injection process in a parent well, mitigate the negative effects of frac-hits, and maximize production of both parent and infill wells.
Yi, Peng (RIPED PetroChina) | Chunming, Xiong (RIPED PetroChina) | Jianjun, Zhang (RIPED PetroChina) | Yashun, Zhang (Data Company of Xin Jiang Oilfield Company PetroChina) | Qinming, Gan (Oil & Gas Technology Research Institute of Changqing Oilfield Company PetroChina) | Guojian, Xu (Data Company of Xin Jiang Oilfield Company PetroChina) | Xishun, Zhang (RIPED PetroChina) | Ruidong, Zhao (RIPED PetroChina) | Junfeng, Shi (RIPED PetroChina) | Meng, Liu (RIPED PetroChina) | Cai, Wang (RIPED PetroChina) | Guanhong, Chen (RIPED PetroChina)
With the depletion of reservoirs, it is inevitable that once very prolific conventional and unconventional wells become stripper wells characterized as producing no more than 35 barrels of liquid equivalent per day over a 12-month period. Conventional metering strategy is not economic for its huge investment on facilities, equipment, sensors and ongoing maintenance compared to current low production. This paper presents an innovative method utilizing state-of-art artificial intelligent algorithms to predict the production rate from real-time IIot testing and producing data with very low cost and reliable accuracy.
Abundant real-time field and well data acquired from IIot and digital field facilities establish a fundamental foundation for developing a machine learning application. This paper presents a method to predict the real-time production rate from real-time IIot data. In our method, we start with constructing our datasets from different data sources by combining the dynamometer cards, pumping stroke and rate, pump, rod, wellbore and reservoir parameters as inputs and the corresponded production rate as targets. The machine-learning model contains two neural networks: first, a deep autoencoder to extract the feature representations from all the dynamometer cards; then another neural network combining all related features to predict the real-time production.
The deep autoencoder derived features from dynamometer cards are used as parts of inputs to further real-time production prediction model, which eliminate the disadvantage of conventional hand-crafted features. Hand-crafted features can lose important information whereas autoencoder is designed to minimize information loss by learning high level features that can be used to reconstruct the cards. The production prediction model with pump and producing data combining more informative abstract features generate a good accordance with the history data. After tested testing and validating data in several fields in one operator's fields in China, the model demonstrate very high accuracy and with R2 more than 0.92, MAE less than 0.5 of wells producing less 5m3/day, RMSE less than 1.6 of wells producing 5-10m3/day and RMSE less than 2.2 of wells producing more than 10m3/day. The model has also been tested on hundreds of newly producing wells and with error in 10%, comparing with high resolution real-time metering equipment.
The method described in this paper can be fully utilized to metering the real-time production with ultra-low cost in wells as long as acquiring real-time dynamometer card. With the fast development of artificial intelligence technology and expanding training datasets, artificial intelligence is a good choice to lower the investment and maintenance cost for conventional and unconventional fields in the low oil price trend.