Human factors are identified as the major contributor to oil and gas drilling and other operations related accidents. Offshore oil and gas operations involve complex scenarios and decision-making with potentially catastrophic consequences. The current simulation-based training modules are often criticized for their lack of objective and validated measures for human factors and non-technical skills. There is also a need to include measures for enhanced situational awareness and decision-making for the offshore drilling crew. In this study, we present holistic human-centered training framework equipped with assessment techniques to analyses situational awareness of partcipants in customized well-control operations.
The training exercise used in this work included real-time well control operation customized for drilling break and kick detection scenarios. The assessment approach consisted of eye-tracking data analysis, questionnaire analysis, checklist score analysis, and communication log analysis. After individual analysis from each technique, a new framework was developed to triangulate results from each technique to provide a comprehensive assessment. The participants included seven group of novices and one group of experts. The preliminary results indicate significant differences between the situation awareness and performance of participants. Furthermore, there were observed notable differences between the perceptual, comprehensive, and projection ability of novices and experts in routine jobs on a drilling platform. The eye-tracking data features included fixation count and fixation duration, and it was inferred that eye-tracking results can be representative of cognitive abilities of the partcipants. Furthermore, the fixation count and duration results were highly correlated with the checklist scores.
Overall, the adopted methodology in this study have potential to open new avenues for human- centered training framework and improvement in traditional assessment approach. Furthermore, it can also be helpful in understanding of cognitive responses of the offshore professionals.
Talos Energy made a big splash in 2015 when it won two of the fourteen blocks offered in Mexico's inaugural Round 1 lease sale. The company and its partners drilled the Zama-1 exploration well in July 2017, the first well drilled offshore by the private sector in Mexico's history, and later announced a massive discovery with over a billion barrels of oil in place. Now the company is moving forward to reach final investment decision and bring the field on line by the early 2020s. Outside of Mexico, Talos has reshaped its portfolio through its recent merger with Stone Energy, becoming public in the process. Following the transaction, the company now operates several US Gulf of Mexico facilities as well as subsea tie-backs.
Weijermans, Peter-Jan (Neptune Energy Netherlands B.V.) | Huibregtse, Paul (Tellures Consult) | Arts, Rob (Neptune Energy Netherlands B.V.) | Benedictus, Tjirk (Neptune Energy Netherlands B.V.) | De Jong, Mat (Neptune Energy Netherlands B.V.) | Hazebelt, Wouter (Neptune Energy Netherlands B.V.) | Vernain-Perriot, Veronique (Neptune Energy Netherlands B.V.) | Van der Most, Michiel (Neptune Energy Netherlands B.V.)
The E17a-A gas field, located offshore The Netherlands in the Southern North Sea, started production in 2009 from Upper Carboniferous sandstones, initially from three wells. Since early production history of the field, the p/z plot extrapolation has consistently shown an apparent Gas Initially In Place (GIIP) which was more than 50% higher than the volumetric GIIP mapped. The origin of the pressure support (e.g. aquifer support, much higher GIIP than mapped) and overall behavior of the field were poorly understood.
An integrated modeling study was carried out to better understand the dynamics of this complex field, evaluate infill potential and optimize recovery. An initial history matching attempt with a simulation model based on a legacy static model highlighted the limitations of existing interpretations in terms of in-place volumes and connectivity. The structural interpretation of the field was revisited and a novel facies modeling methodology was developed. 3D training images, constructed from reservoir analogue and outcrop data integrated with deterministic reservoir body mapping, allowed successful application of Multi Point Statistics techniques to generate plausible reservoir body geometry, dimensions and connectivity.
Following a series of static-dynamic iterations, a satisfying history match was achieved which matches observed reservoir pressure data, flowing wellhead pressure data, water influx trends in the wells and RFT pressure profiles of two more recent production wells. The new facies modeling methodology, using outcrop analogue data as deterministic input, and a revised seismic interpretation were key improvements to the static model. Apart from resolving the magnitude of GIIP and aquifer pressure support, the reservoir characterization and simulation study provided valuable insights into the overall dynamics of the field – e.g. crossflows between compartments, water encroachment patterns and vertical communication. Based on the model a promising infill target was identified at an up-dip location in the west of the field which looked favorable in terms of increasing production and optimizing recovery. At the time of writing, the new well has just been drilled. Preliminary logging results of the well will be briefly discussed and compared to pre-drill predictions based on the results of the integrated reservoir characterization and simulation study.
The new facies modeling methodology presented is in principle applicable to a number of Carboniferous gas fields in the Southern North Sea. Application of this method can lead to improved understanding and optimized recovery. In addition, this case study demonstrates how truly integrated reservoir characterization and simulation can lead to a revision of an existing view of a field, improve understanding and unlock hidden potential.
A flow simulation-driven time-lapse seismic feasibility study is performed for the Amberjack field that leverages existing multi-vintage 4D time-lapse seismic data. The focus is a field consisting of stacked shelf and deepwater reservoir sands situated in the Gulf of Mexico in Mississippi Canyon Block 109 in 1,030 ft of water. The solution leverages seismic interpretation, seismic inversion, earth modeling, and reservoir simulation [including embedded petro-elastic modeling (PEM) capabilities] to enable the reconciliation of data across multiple seismic vintages and forecast the optimal future seismic survey acquisition in a closed-loop. The overarching feasibility solution is integrated and simulation-driven involving multi-vintage seismic inversion, spatially constraining the petrophysical property model by seismic inversion, and performing reservoir simulation with the embedded PEM. The PEM is used to compute P-impedance and Vp/Vs dynamically, which enables tuning to both historical production and multi-vintage seismic data. The process considers a hybrid fine-scale 3D geocellular model in which the only upscaling of petrophysical properties occurs when the P-impedance from seismic inversion is blocked to the 3D geocellular grid. This process minimizes resampling errors and promotes direct tuning of the simulator response with registered seismic that has been blocked to a geocellular earth model grid. The results illustrate a three-part simulation-to-seismic calibration procedure that culminates with a prediction step which leads to a simulation-proposed time-lapse seismic acquisition timeline that is consistent with the calibrated reservoir simulation model. The first calibration tunes the model to historical production profiles. The second calibration reconciles the dynamic P-impedance estimate of the simulated shallow reservoir with that of the seismic inversion blocked to the 3D geocellular grid. The combination of these two steps outline a seismic-driven history matching process whereby the simulation model is not only consistent with production data but also the subsurface geologic and fluid saturation description. Large and short wavelength disparities in the P-impedance calibration existing between the simulator response and the time-lapse seismic data are attributed to resampling errors as a result of seismic inversion-derived P-impedance being blocked to the 3D geocelluar grid, as well as sparse well control in the earth model which leads to the obscuring of some asset-specific characteristics. The results of the third calibration step show how the time-lapse seismic feasibility solution accurately confirms prior seismic surveys undertaken in the asset. Given this confirmation, the solution achieves a suitable prediction of seismic-derived rock property response from the reservoir simulator as well as the optimal future time-lapse seismic acquisition time.
After the 2010 subsea blowout of the Macondo well that resulted in 11 deaths and the worst oil spill in US history, early kick detection technology found itself at the top of the wish list for regulators and offshore companies seeking to avoid similar accidents. The offshore industry has taken another step toward opening up new deepwater frontiers to exploration with Maersk Drilling ordering the first 20,000-psi blowout preventer (BOP) made by GE Oil and Gas. Many problems that result in BOP downtime could be prevented if only drilling contractors knew which parts of the subsea system to replace and when. BOP monitoring systems have been developed to increase reliability by enabling preventive maintenance.
When people hear of surfactant research in the oil and gas industry, they most likely think of surfactant flooding. An emerging area of surfactant application in the industry is as a conformance improvement agent. Digitalization is going to impact every industry in the next 5–10 years. The oil and gas industry needs a lot more data scientists today than a year ago, so a person with the right qualifications and experience is the need of the industry today. Author of the Pulitzer Prize winner The Prize, Daniel Yergin is an energy luminary whose achievements promote the international understanding of energy and its economics.
Africa (Sub-Sahara) Eni discovered up to 250 million bbl of light oil in the Ndungu exploration prospect in Block 15/06 offshore Angola. A well in 1076 m of water reached TD of 4050 m and proved a single oil column of approximately 65 m with 45 m of net pay of 35 API oil. Well results indicate production capacity in excess of 10,000 B/D. Eni operates Block 15/06 with 36.8421% Joint venture partners are Sonangol P&P (36.8421%) and SSI Fifteen (26.3158%). Eni discovered gas and condensate on the Akoma prospect in CTP-Block 4 offshore Ghana. The Akoma-1X exploration well was drilled in 350 m of water approximately 50 km offshore and 12 km northwest of the FPSO John Agyekum Kufuor.
Appomattox begins production below cost and ahead of schedule in another optimistic sign of the offshore sector’s rebound. Shell made its sixth discovery from the Norphlet formation in the deepwater Gulf of Mexico, where the firm’s newly arrived Appomattox platform just 13 miles away presents a tieback opportunity.
Anchored by the Khaleesi-Mormont and Samurai fields, the King’s Quay FPS will receive and process up to 80,000 B/D of crude oil. Murphy Oil to Buy Deepwater US Gulf Assets for up to $1.625 Billion The El Dorado, Arkansas-based Murphy has quickly found a home for some of the cash it will receive from the sale of its Malaysia business. The company has been rapidly expanding its US gulf footprint while simplifying its portfolio and targeting more oil. PTTEP to Buy Murphy Oil’s Malaysian Business for $2.1 Billion Thailand’s PTTEP is doubling down on Malaysian oil and gas in an effort to broaden its reach in its native Southeast Asia. The new venture will operate several deepwater assets—including the Cascade, Chinook, Medusa, and Thunder Hawk fields.