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Sempra Energy’s Energía Costa Azul LNG (ECA LNG) subsidiary reached a final investment decision (FID) to build its $2-billion Phase 1 natural gas liquefaction export project in Baja California, Mexico. ECA LNG, a joint venture between Sempra LNG and its Mexico subsidiary IEnova, is the only LNG export project to reach FID in 2020, and is slated to be the first on the Pacific Coast of North America. The facility will connect natural gas supply from Texas and the western US to Mexico and other countries across the Pacific Basin. First production from Phase 1 is expected in late 2024. The company secured a 20-year supply agreement with Mitsui and an affiliate of Total for the purchase of 2.5 mtpa and is working with Total for a potential equity investment in the facility.
Petrobras is offering a 50% stake in its Marlim complex located offshore Brazil in the Campos Basin. The deal is part of the company’s plan to optimize its portfolio, improve capital allocation, and shift its resources to deep and ultradeep waters. Marlim is the third offer the company has made over the past five weeks following its notices for the Bahia Terra and Carmópolis clusters. The offshore development is the third largest in Brazil and fourth largest in the Americas by production, with potential for future activity including near-term field revitalization and pre-salt potential. It’s also the largest post-salt development in Brazil with more than 20 billion bbl of original oil in place (OOIP) and includes licenses through 2052 with potential to increase the current production of approximately 120 million BOE/D by about 60%.
As the oil and gas industry enters the digital era, openness is a key enabler to realizing the vision of transforming the industry for the better. The practice of reservoir engineering and reservoir simulation is no exception. In this paper, an openness mechanism in a reservoir simulator using Python scripting language is introduced. It empowers engineers to utilize simulation in new ways. It extends simulator capabilities and enables people to implement flexible-control logic to solve field management challenges.
The new openness mechanism in the simulator allows engineers to program and include Python scripts in a simulation model. These scripts interrogate and interact with the simulator. The scripts are executed by the simulator while running the model. Flexibility is available to execute the scripts at every Newton iteration, before and after every simulation timestep, at specified times, or when a criterion is met. Simulation model properties can be queried through the scripts, such as well connections, well properties, group properties, grid region properties, network entities, current simulation time, etc. The scripts can set properties such as well constraints, well and connection productivity index (PI), group production target, pausing or stopping the run, etc. Customized control logic, if not directly available in the simulator, can be implemented in the scripts that interrogate and drive the simulator. Such customization can be packaged as Python libraries and shared with team members, enabling continuous value creation. Public Python libraries, such as NumPy, pandas and pywin32 or win32com, can also be loaded in the scripts to extend even further what the simulator can do.
The openness mechanism is demonstrated on case examples. They include customized action to acidize wells when production drops, approximating the geomechanical effect of decreasing well pressure, modeling the effect of fines in injected water on well injectivity, and connecting to a network simulator. Examples are also given on customized reporting for model diagnostics and result interpretation, setting production constraints based on economics calculated in an Excel sheet with complex fiscal regime, advanced gas accounting, management of sulphur content, dual-optimization to meet gas demand while honoring oil treatment capacity, and integrated asset modeling from reservoir to surface networks to processing facilities.
the ability to extend built-in functionalities of a reservoir simulator and customize field management controls using user scripting language. It embraces innovation and enables continuous value creation in reservoir simulation.
ADNOC LNG signed a supply agreement for up to 6 years with Vitol for the sale of 1.8 mtpa of post-2022 LNG volumes, and a 2-year supply agreement with Total for 0.75 mtpa of 2021 and 2022 LNG volumes. The agreements continue ADNOC’s transition to a multi-customer strategy that began in 2019, and follow its investment partnership with Vitol in global storage terminal owner and operator VTTI. Since then, the company shifted from supplying 90% of its LNG to Japan to supplying 90% of LNG to clients in more than eight countries from across southern and southeast Asia. The agreement is also in line with its 2030 gas strategy to deliver value for UAE and meet global demand, which is expected to grow by up to 5% annually over the next 20 years. ADNOC LNG, owned by ADNOC (70%), Mitsui & Co (15%), BP (10%), and Total (5%), produces about 6 mtpa of LNG from its Das Island facilities off the coast of Abu Dhabi.
Salazar Aldana, Samuel Francisco (PEMEX) | Hernández Sánchez, Rogelio (PEMEX) | Alviso Zertuche, Xavier Omar (Schlumberger) | Munoz Rivera, Moises (Schlumberger) | Camarillo Valtierra, Jose Luis (Schlumberger) | Andrade Sierra, Emmanuel Antonio (Schlumberger) | Santini Perez, Jesus del Carmen (Schlumberger) | Anleu, Pedro Leonel (Schlumberger) | Resendiz Torres, Jesus Tadeo (Schlumberger)
Water production represents a major challenge over the life of a reservoir. It is an important issue that directly affects hydrocarbon production and total reserves recovery around the world, especially in fractured reservoirs. In the south of Mexico, several naturally fractured, low-pressure reservoirs experience production disruptions when water from the aquifer channels invades oil-producing intervals through high-conductivity fractures.
Water shutoff (WSO) treatments vary in design approach and efficacy percentage due to the difference in environments and formations that are subject to water breakthrough. For the last decade, in southern Mexico, different treatments have been performed without achieving the expected results in the described reservoirs. These treatments have included different types of fluids, including rigid setting gels; reactive pills; selective water setting cement; and conventional cement slurries, with or without the use of mechanical aids such as mechanical plugs, cement retainers, or coiled tubing for precise placement.
One of the biggest challenges of WSO in these reservoirs is that the proposed treatments must have a high level of penetration into the natural fractures but, at the same time, they need to be displaced with nitrogen or light hydrocarbon derivatives to balance reservoir pressure, avoiding total losses of fluids into the highly conductive, low-pressure reservoir where they will lose the ability to control water flow from the aquifer.
Using the synergies of the operator's reservoir knowledge, diagnostic workflow, and historical treatment records coupled with service company's treatment engineering technologies and local ability to manipulate and enhance existing WSO fluids, we exercised a systematic evaluation approach to the evaluation of past unsuccessful experiences and proposed adjustments to conventional treatments using rigid gel and conventional cement slurries for water control.
Integrating the relevant findings following operator's water control diagnostic workflow with the study of relevant papers and methodologies used by oil companies around the globe, we proposed a different treatment strategy consisting in the addition of a reactive pill between the rigid gel and the cement to keep the treatment in the vicinity of the wellbore, viscous spacers between each treatment fluid to avoid contamination while traveling downhole, the inclusion of lost circulation fibers to create a fibrous net to promote cement filter-cake development and tailored treatment displacement with a predefined pressure according to reservoir condition that is close to the reservoir-equivalent hydrostatic pressure.
During the past 2 years, the application of rigorous evaluation of potential candidates and the combination of these three enhanced WSO fluids in the described sequence reduced unwanted water production in two naturally fractured low-pressure reservoirs. In three field cases, the use of the proposed methodology led to a reduction of overall water production from an initial value between 70 and 100% to levels below 30%. Incremental oil production has been maintained in the best cases for more than 2 years after the treatments. The most significant result occurred in the first field case, where the water cut was reduced from 90% to less than 2% and oil production increased 12 times, obtaining a cumulative oil production of 240,000 bbl in a year.
The documented methodology is a work in progress; we cannot replicate the technique exactly because each well presents challenges according to its construction and structural placement. Similar WSO treatments have been successfully applied in several wells in southern Mexico, increasing oil production and recoverable reserves. Continuous improvement efforts have also led to efficiency enhancement over time, as results and lessons learned are captured to be shared and replicated in similar reservoirs.
The Abu Dhabi National Oil Company (ADNOC) completed the first phase of its large-scale multiyear predictive maintenance project to improve asset efficiency and integrity across its upstream and downstream operations. Announced in November 2019, the project is being implemented over four phases as part of the company’s digital acceleration program to embed advanced digital technologies across its operations. Phase 1 covers the modeling and monitoring of 160 turbines, motors, centrifugal pumps, and compressors across six ADNOC Group companies. All phases of the project are expected to be completed by 2022 and will enable monitoring of up to 2,500 critical machines. Using artificial intelligence (AI) technologies including machine learning and digital twins, the company’s predictive maintenance platform helps with equipment stoppages, reduces unplanned equipment maintenance and downtime, increases reliability and safety, and is expected to deliver maintenance savings up to 20%.
One of the major objectives of seismic interpretation is to effectively predict the distribution of reservoir facies away from well control. With the advent of increasing number of meaningful seismic attributes, it is time consuming and laborious to analyze them through conventional analytical methods. Machine learning techniques analyze higher dimensional data points faster and effectively. Automated seismic facies classification techniques are increasingly becoming important in identifying the potential hydrocarbon bearing zone and favorable facies. Such facies classification techniques, or, automated clustering algorithms, help arrange similar seismic traces based on the waveform shape, amplitude, phase, frequency, and other relevant seismic attributes. The main objective of automated facies classification using machine learning techniques is to perform facies classification fast and efficiently using several relevant seismic attributes for mapping the facies distribution and effective identification of the sweet spots.
The automated clustering algorithms fall into two categories – supervised and unsupervised algorithms. Unsupervised machine learning algorithms are purely data driven and help in recognizing and classifying the patterns from a dataset without any a priori information. A posteriori information such as well data, is integrated into the results for recognizing the facies classification and calibrating the interpretation. Unsupervised learning methods also help to highlight subtle stratigraphic features that might otherwise be unnoticed using conventional analytical methods.
In this study, we adopted a recent unsupervised classification technique called, generative topographic mapping (GTM). We applied this technique to a dataset from Offshore Nova Scotia, to extract the natural clusters from the seismic data for facies classification. Using the GTM technique applied to seismic data, we were able to map the distribution of different facies and potential sweet spots in the study area.
The evaluation of injection efficiency is an important component of monitoring oilfield development; it is often complicated by well design, such as horizontal wells, dual or triple completions, and the use of inflow control devices (ICDs). This paper demonstrates how standard production logging (PL) measurements can be improved with the addition of a high sensitivity noise log and a quantitative temperature analysis that is sensitive to flow behind the tubing and casing.
The latest generation noise tools are highly sensitive instruments that record data during multiple short duration stations. The tools acquire noise energy across a wideband spectrum ranging from 8 to 60,000 Hz. To improve interpretation and visualization, the noise data is separated into two displays: an ultra-low frequency narrowband spectrum (8 to 4,000 Hz) and a wideband spectrum (100 to 60,000 Hz). The implementation of high-resolution temperature data with thermal modeling enables improved multiphase flow profiling using the Joule-Thomson effect and near-wellbore temperature behavior in the surrounding formations.
The integration of the noise tool and temperature modelling enables the detection of very low rate fluid flow that is not otherwise detectable with conventional production logging tools (PLT) and the quantification of multiphase flow behind the pipe in multi-barrier completions.
This paper includes three case studies that demonstrate the integration of noise spectral data, temperature modeling, and spinner data. The first case is a single-string vertical injector well with separate injection at various layers. In this case, the well completion was challenging because the upper production interval was located behind the tubing. In the second example, the injection distribution changes under different flow rate conditions. At a high injection rate, the formation fracture pressure is exceeded; at low flow, the injection is below the fracture pressure.
These results demonstrate that it is possible to qualitatively determine all injection intervals by noise logging and to quantitatively estimate the fluid distribution by thermal modeling, which was used to plan successful well workovers and restore injection. This technology identified zones that were taking injection that other conventional tools failed to identify, significantly improving the understanding of water injection conformance in the super giant Burgan field waterflood.
Through the end of October, 38 rigs (jackups, semisubmersibles and drillships) have been retired in 2020. Of that total, 24 were 30 years or older, but four of the five drillships removed from the fleet were 10 years of age or younger, with the fifth only a few months over 10 years. The 9.2-year average attrition age for drillships in 2020, as shown in Figure 1, is a stark contrast to 36 years for the 17 jackups and 27 years for the 16 semisubmersible retirements. The five drillships retired so far in 2020 were owned by Valaris and Noble Drilling. Looking at their history, there are a few eye-catching numbers, but one stands out.
The Lower Cretaceous carbonates of the Shuaiba Formation forms one of the most prolific carbonate reservoirs in the region. These carbonates were deposited on a shallow epeiric carbonate platform during the Aptian. In addition, intra-shelf basins formed on this vast platform where deeper and more restricted carbonates were deposited. As a result of a long term relative sea-level fall these carbonates started to prograde into the intra-shelfal Bab Basin. The platform ultimately diminished during exposure of the Shuaiba platform and subsequent flooding and the deposition of deeper water carbonates and clastics of the Nahr Umr Formation. The observations described here concentrate on the sequence stratigraphy of the shallow carbonate platform top carbonates in outcrop and the subsurface.
The internal succession of the Shuaiba Formation can be briefly described in core and outcrop as follows: The Shuaiba Formation has been deposited on top of the deeper-water carbonates of the Hawar member, which terminated the Kharaib platform. Based on core evidence from several Abu Dhabi oilfields, the Lower Shuaiba Fm. is composed of two depositional cycles composed of
Establishing a sequence stratigraphic framework on shallow carbonate platforms heavily relies on identifying sequence boundaries (SBs), which are the best preserved surfaces recognized in these platform settings. Deposits as a result of maximum flooding surfaces are often not distinct since bathymetry was never deep enough on shallow-water platforms to deposit deep water carbonate mudstones and shales.
The two most recognizable sequence boundaries for the Shuaiba Fm., indicating widespread regional exposure, are: the solution collapse breccia on top of the
This interpretation follows common sequence stratigraphic rules and helps to better understand the laterally extensive water break-through zones in producing fields. Consequently, some of the previous interpretations on the Shuaiba shallow platform top carbonates require reconsideration.