scholarly journals GEOLOGICAL AND GEOPHYSICAL DATA OF “EPSILON” FIELD IN PRINOS OIL BASIN

2017 ◽  
Vol 43 (5) ◽  
pp. 2257
Author(s):  
Y. Mertzanides ◽  
E. Kargiotis ◽  
A. Mitropoulos
Keyword(s):  
Aegean Sea ◽  
Oil Field ◽  
Geophysical Data ◽  
Geochemical Data ◽  
Depositional Model ◽  
Drilling Data ◽  
Geological Information ◽  
Drilling Operations ◽  
Miocene Tectonics ◽  
Tectonics And Sedimentation

The Epsilon field, is located at the centre of Prinos oil basin (N. Aegean, Greece), 11 km NW of the island of Thassos and 4 km NW of the Prinos field, the first productive oil field in the Aegean Sea. The taphrogenetic basin of Prinos has been widely studied, due to its hydrocarbon reservoirs. Extensive geophysical survey, started at early 1970 ‘s, led to a number of drilling jobs, which confirmed the existence of hydrocarbons in the area. The combined geological information, derived from the analysis of lithological, stratigraphic and geochemical data of the basin, suggested a structural and depositional model, strongly related to the Miocene tectonics and sedimentation. The new geophysical and drilling data from Epsilon oil field, are correlated to that already known, completing the model of the basin. Pay zone is found to be below an evaporitic sequence, consisting predominantly of salt, with anhydrite, clay and sandstone intercalations. These upper Miocene aged evaporites extend, varying in thickness, throughout Prinos basin. Reservoir consists mainly of sandstone with intercalations of claystone and trace of siltstone. The geology of the structure and the initial productivity, were positive for further drilling operations in Epsilon field.

Investigation of the Internal Blowout Accident Involving Overpressured Reservoirs: Case of CI-11 Well, Southern Tunisia

2021 ◽  
pp. 1-14
Author(s):  
Chaouki Khalfi ◽  
Riadh Ahmadi
Keyword(s):  
Water Flow ◽  
Water Table ◽  
Action Plan ◽  
Oil Field ◽  
Hot Water ◽  
Shallow Water Table ◽  
Ecological Consequences ◽  
Southern Tunisia ◽  
Drilling Operations ◽  
Recovery Program

Summary This study consists of an assessment of the ecological accident implicating the Continental Intercalaire-11 (CI-11) water well located in Jemna oasis, southern Tunisia. The CI-11 ecological accident manifested in 2014 with a local increase of the complex terminal (CT) shallow water table salinity and temperature. Then, this phenomenon started to spread over the region of Jemna, progressively implicating farther wells. The first investigation task consisted of logging the CI-11 well. The results revealed an impairment of the casing and cement of a huge part of the 9⅝ in. production casing. Historical production records show that the problems seem to have started in 1996 when a sudden production loss rate occurred. These deficiencies led to the CI mass-water flowing behind the casing from the CI to the CT aquifers. This ecological accident is technically called internal blowout, where water flows from the overpressurized CI groundwater to the shallower CT groundwater. Indeed, the upward CI hot-water flow dissolved salts from the encountered evaporite-rich formations of the Lower Senonian series, which complicated the ecological consequences of the accident. From the first signs of serious water degradation in 2014 through the end of 2018, several attempts have been made to regain control of annular upward water flow. However, the final CT groundwater parameters indicate that the problem is not properly fixed and communication between the two involved aquifers still persists. This accident is similar to the OKN-32 case that occurred in the Berkaoui oil field, southern Algeria, in 1986, and included the same CI and CT aquifers. Furthermore, many witnesses claim that other accidental communications are probably occurring in numerous deep-drilled wells in this region. Concludingly, Jemna CI-11, Berkaoui OKN-32, and probably many other similar accident cases could be developing regional ecological disasters by massive water resource losses. The actual situation is far from being under control and the water contamination risk remains very high. In both accidents, the cement bond failure and the choice of the casing point are the main causes of the internal blowout. Therefore, we recommend (1) a regional investigation and risk assessment plan that might offer better tools to predict and detect earlier wellbore isolation issues and (2) special attention to the cement bond settlement, evaluation, and preventative logging for existing wells to ensure effective sealing between the two vulnerable water table resources. Besides, in the CI-11 well accident, the recovery program was not efficient and there was no clear action plan. This increased the risk of action failure or time waste to regain control of the well. Consequently, we suggest preparing a clear and efficient action plan for such accidents to reduce the ecological consequences. This requires further technical detailed study of drilling operations and establishment of a suitable equipment/action plan to handle blowout and annular production accidents.

scholarly journals The Mesozoic of the South Caspian oil and gas province, Southwestern Turkmenistan – prospects for gas and oil exploration

2021 ◽  
pp. 102-133
Author(s):  
V.N. Melikhov ◽  
N.A. Krylov ◽  
I.V. Shevchenko ◽  
V.L. Shuster
Keyword(s):  
Oil And Gas ◽  
High Efficiency ◽  
State Of The Art ◽  
Oil Field ◽  
Seismic Exploration ◽  
Oil Exploration ◽  
The South ◽  
Analytical Review ◽  
Drilling Operations ◽  
Low Efficiency

Regarding the South Caspian oil and gas province, it is concluded that the Pliocene productivity prevails in the western part of the province, and that the gas and oil prospects of the eastern land side in the Mesozoic are prioritized. A retrospective analytical review of geological and geophysical data and publications on the Mesozoic of Southwestern Turkmenistan was carried out, which showed the low efficiency of the performed seismic and drilling operations in the exploration and evaluation of very complex Mesozoic objects. A massive resumption of state-of-the-art seismic exploration and appraisal drilling in priority areas and facilities performed by leading Russian companies is proposed. For some areas, a new, increased estimate of the projected gas resources is given. An example of modern high-efficiency additional exploration of the East Cheleken, a small Pliocene gas and oil field, which turned this field into a large one in terms of reserves, is given.

Improving Real-Time Drilling Data Quality Using Artificial Intelligence and Machine Learning Techniques

2021 ◽  
Author(s):  
S. H. Al Gharbi ◽  
A. A. Al-Majed ◽  
A. Abdulraheem ◽  
S. Patil ◽  
S. M. Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Input Data ◽  
Support Vector ◽  
The Real ◽  
Drilling Data ◽  
Drilling Operations

Abstract Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations. To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality. To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.

Estimation Update and Feedback Procedures

2008 ◽  
Author(s):  
P.J. Lee
Keyword(s):  
Water Saturation ◽  
Geophysical Data ◽  
Source Rocks ◽  
Geochemical Data ◽  
Burial History ◽  
Data Types ◽  
Petroleum Resource ◽  
History Of ◽  
Seismic Sections ◽  
Well Data

A basin or subsurface study, which is the first step in petroleum resource evaluation, requires the following types of data: • Reservoir data—pool area, net pay, porosity, water saturation, oil or gas formation volume factor, in-place volume, recoverable oil volume or marketable gas volume, temperature, pressure, density, recovery factors, gas composition, discovery date, and other parameters (refer to Lee et al., 1999, Section 3.1.2). • Well data—surface and bottom well locations; spud and completion dates; well elevation; history of status; formation drill and true depths; lithology; drill stem tests; core, gas, and fluid analyses; and mechanical logs. • Geochemical data—types of source rocks, burial history, and maturation history. • Geophysical data—prospect maps and seismic sections. Well data are essential when we construct structural contour, isopach, lithofacies, porosity, and other types of maps. Geophysical data assist us when we compile number-of-prospect distributions and they provide information for risk analysis.

Russian Arctic Basins: Probability of Giant Oil Field Discoveries in the Light of New Geophysical Data

2019 ◽  
Author(s):  
A. Piskarev ◽  
V. Kaminsky ◽  
V. Poselov ◽  
V. Savin ◽  
O. Smirnov
Keyword(s):  
Oil Field ◽  
Geophysical Data ◽  
Russian Arctic

THE TUBRIDGI GAS FIELD REJUVENATED

2001 ◽  
Vol 41 (1) ◽  
pp. 429
Author(s):  
R.J.W. Bunt ◽  
W.D. Powell ◽  
T. Scholefield
Keyword(s):  
Depth Map ◽  
Geophysical Data ◽  
Depth Image ◽  
Low Permeability ◽  
Geological Model ◽  
Structural Configuration ◽  
Gas Field ◽  
Structural Character ◽  
Variable Nature ◽  
Geological Information

Difficulties in defining the structural character of the reservoir horizons at the Tubridgi Gas Field arise from gas charging of thin, often laterally discontinuous, silts and sands within the overburden. The gas charging of these shallow, low permeability units results in a seismic representation of the field as a time low. Historically, conversion from time to a reliable depth image has been problematic due to the variable nature of the gas charging, the relatively sparse, multi-vintage 2D seismic coverage and the corresponding difficulties in defining an accurate velocity field.After the unsuccessful drilling program in 1997 when three out of the five wells were plugged and abandoned, a revised interpretation methodology was developed, incorporating all available geophysical data, but placing a much greater emphasis on geological information from each of the wells in the area.The new depth map and geological model were tested by the drilling of Tubridgi–16 to –18 in August 1999. These three wells intersected the Birdrong Sandstone within one metre of prognosis, with two wells located structurally up-dip of the previous 17 wells drilled on the field. This accuracy resulted in a 97% increase in remaining reserves and a much higher level of confidence in the structural configuration of the Tubridgi field.A core of the Lower Gearle Sandstone in the Tubridgi 18 well highlighted the potential of this zone which has subsequently been evaluated in greater detail and potentially represents an additional productive horizon for the field.

Tracing migration pathways by integrated geological, geophysical, and geochemical data: A case study from the JX1-1 oil field, Bohai Bay Basin, China

AAPG Bulletin ◽  
2014 ◽  
Vol 98 (10) ◽  
pp. 2109-2129 ◽  
Author(s):  
Shang Xu ◽  
Fang Hao ◽  
Changgui Xu ◽  
Huayao Zou ◽  
Jinqiang Tian
Keyword(s):  
Oil Field ◽  
Geochemical Data ◽  
Bohai Bay ◽  
Bohai Bay Basin ◽  
Migration Pathways
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