Below is a list of basins and fields; however this is a short list since there are more than 65,000 oil and gas basins and fields of all sizes in the world. However, 94% of known oil fields is concentrated in fewer than 1500 giant and major fields. Most of the world's largest oilfields are located in the Middle East, but there are also supergiant ( 10 billion bbls) oilfields in India, Brazil, Mexico, Venezuela, Kazakhstan, and Russia. Add any basins or fields that are missing from this list!
This section features industry photographs submitted by SPE members. Selected pictures will be posted on the website. Be sure to provide your full name, job position, company name, picture location, and a caption for the picture. I don't know how anyone could see a sight like this rig and not marvel at its sheer size, design, in... This picture is of a remote field Reliance Production Optimization operates for a client.
Africa (Sub-Sahara) BG Group discovered gas in the Taachui-1 well and sidetrack in Block 1, offshore Tanzania. The drillship Deepsea Metro Idrilled Taachui-1 close to the western boundary of Block 1, then sidetracked the well and drilled to a total depth of 4215 m. The well encountered gas in a single gross column of 289 m within the targeted Cretaceous reservoir interval. Net pay totaled 155 m. Estimates of the mean recoverable gas resources are around 1 Tcf. Statoil (65%) and co-venturer ExxonMobil (35%) made a sixth discovery--the Piri-1 well--in Block 2 offshore Tanzania. Piri-1 was drilled by drillship Discoverer Americas, at a water depth of 2360 m.
Africa (Sub-Sahara) Eni finished a production test on its Minsala Marine 1 NFW well, located in Marine XII block, 35 km offshore The Republic of the Congo. During the test, the well delivered natural flow in excess of 5,000 B/D of 41 API crude and 14 MMcf/D of natural gas from a 37-m opened section of the discovery's 420-m column. Eni (65%) is operator, with state-owned partner SNPC (25%), and New Age (African Global Energy) Limited (10%). Asia Pacific CNOOC started natural gas production from the Panyu 34-1/35-1/35-2 project at the Pearl River Mouth basin in the South China Sea. Main production facilities for the three gas fields include one comprehensive platform, two sets of underwater production systems, and 13 producing wells. Two wells are producing a total of 21 MMcf/D of gas. The project is expected to reach peak production of 150 MMcf/D.
Africa (Sub-Sahara) Vaalco Energy started oil production from the Etame 12-H development well offshore Gabon. The well was drilled to a measured depth of approximately 3450 m and was targeting the recently discovered lower lobe of the Gamba reservoir. It was brought on line at a rate of 2,000 BOPD with no indication of hydrogen sulfide. Vaalco (28.07%) is the operator with partners Addax Petroleum (31.63%), Sasol (27.75%), Asia Pacific KrisEnergy started drilling the Rossukon-2 exploration well on Block G6/48 in the Gulf of Thailand, using the Key Gibraltar jackup rig. The well will reach a total depth at 5,462 ft and will test Early Miocene stacked fluvial sandstones on a broad structural high.
Africa (Sub-Sahara) Petroceltic International said that the first of up to 24 new development wells planned in Algeria's Ain Tsila gas and condensate field was successful. The AT-10 well, situated about 2 miles from the AT-1 field discovery well, reached a total depth of 6,578 ft. Wireline logs indicated that the expected initial offtake rate would be comparable to the AT-1 and AT-8 wells, both of which test-flowed at more than 30 MMcf/D. Petroceltic is the operator with a 38.25% interest in the production-sharing contract that covers the Ain Tsila output. The remaining interests are held by Sonatrach (43.375%) and Enel (18.375%). Sonangol reported that it has found reserves in the Kwanza Basin of Angola that could total 2.2 billion BOE, including reserves in a block jointly owned with BP. Block 24, operated by BP, holds an estimated 280 million bbl of condensate and 8 Tcf of gas, totaling 1.7 billion BOE, Sonangol said in a statement seen by Reuters.
With the current applications of CO2 in oil wells for enhanced oil recovery (EOR) and sequestration purposes, the dissolution of CO2 in the formation brine and the formation of carbonic acid is a major cause of cement damage. This degradation can lead to non-compliance with the functions of the cement as it changes compressive and shear bond strengths and porosity and permeability of cement. It becomes imperative to understand the degradation mechanism of cement and methods to reduce the damage such as the addition of special additives to improve the resistance of cement against acid attack. Hence, the primary objective of this study is to investigate the effects of hydroxyapatite on cement degradation.
To investigate the impacts of hydroxyapatite additive on oil well cement performance, two Class H cement slurry formulations (baseline/HS and hydroxyapatite containing cement/HHO) were compared after exposure to acidic environments. To evaluate the performance of the formulations, samples were prepared and aged in high-pressure high-temperature (HPHT) autoclave containing 2% brine saturated with mixed gas containing methane and carbon dioxide. Tests were performed at different temperatures (38 to 221°C), pressures (21 to 63 MPa) and CO2 concentrations (10 to 100%). After aging for 14 days at constant pressure and temperature, the samples were recovered and their bond and compressive strength, porosity and permeability were measured and compared with those of unaged samples.
The results demonstrated that adding hydroxyapatite limits carbonation. Baseline samples that do not contain hydroxyapatite carbonated and consequently their compressive strength, porosity, permeability, and shear bond strength significantly changed after aging while hydroxyapatite-containing samples displayed a limited change in their properties. However, hydroxyapatite-containing samples exhibit high permeability due to the formation of microcracks after exposure to carbonic acid at high temperature (221°C). The formation of microcracks could be attributed to thermal retrogression or other phenomena that cause the expansion of the cement.
This article sheds light on the application of hydroxyapatite as a cement additive to improve the carbonic acid resistance of oil well cement. It presents hydroxyapatite containing cement formulation that has acceptable slurry properties for field applications and better carbonic acid resistance compared to conventional cement.
A software system based on deep neural network (DNN) technology was designed and trained to recognize fault lines in 2D seismic vertical sections, and fault surfaces in 3D seismic cubes. The system was successfully tested on public domain data from several basins in New Zealand. The paper describes the key components of the system and explains how they were designed. A relatively small size window is used to scan 2D seismic sections. Two DNNs identify if the window contains a fault and output the vector corresponding to the fault segment in the window. After scanning the entire section, a clustering algorithm is applied to group these vectors in separate clusters corresponding to the faults in the section. Finally, a linear regression algorithm calculates the fault lines – not always straight – in the section. Fault lines in successive 2D vertical sections of a 3D cube are associated to form fault surfaces. The training data set that was created to train the DNNs contains 120,000 examples. The validation test set has 30,000 examples. A special workflow and software were developed to generate these 150,000 labelled examples with a mix of synthetic and real data. The results obtained are quite satisfactory: the success rate exceeds 95% on the validation test set. The vector clustering algorithm properly handles crossing faults. The system is designed to quickly learn to correct mistakes highlighted by geophysicists, like patterns wrongly identified as faults, or missed faults. This system was trained to identify fault patterns in seismic data just based on examples. The concept can easily be expanded to recognize many other types of patterns such as structural or stratigraphic traps, horizons, and types of seismic facies. This fault detection software is the first step toward a machine learning-based system for automated structural interpretation of seismic data.
Petrophysics is a pivotal discipline that bridges engineering and geosciences for reservoir characterization and development. New sensor technologies have enabled real-time streaming of large-volume, multi-scale, and high-dimensional petrophysical data into our databases. Petrophysical data types are extremely diverse, and include numeric curves, arrays, waveforms, images, maps, 3-D volumes, and texts. All data can be indexed with depth (continuous or discrete) or time. Petrophysical data exhibits all the "7V" characteristics of big data, i.e., volume, velocity, variety, variability, veracity, visualization, and value. This paper will give an overview of both theories and applications of machine learning methods as applicable to petrophysical big data analysis.
Recent publications indicate that petrophysical data-driven analytics (PDDA) has been emerging as an active sub-discipline of petrophysics. Field examples from the petrophysics literature will be used to illustrate the advantages of machine learning in the following technical areas: (1) Geological facies classification or petrophysical rock typing; (2) Seismic rock properties or rock physics modeling; (3) Petrophysical/geochemical/geomechanical properties prediction; (3) Fast physical modeling of logging tools; (4) Well and reservoir surveillance; (6) Automated data quality control; (7) Pseudo data generation; and (8) Logging or coring operation guidance.
The paper will also review the major challenges that need to be overcome before the potentially game-changing value of machine learning for petrophysics discipline can be realized. First, a robust theoretical foundation to support the application of machine leaning to petrophysical interpretation should be established; second, the utility of existing machine learning algorithms must be evaluated and tested in different petrophysical tasks with different data scenarios; third, procedures to control the quality of data used in machine leaning algorithms need to be implemented and the associated uncertainties need to be appropriately addressed. The paper will outlook the future opportunities of enabling advanced data analytics to solve challenging oilfield problems in the era of the 4th industrial revolution (IR4.0).