The main objective of this paper is to present a proper DST design for deepwater gas wells with potential gas hydrate problems because of low seabed temperature.Prior to discussing the DST procedures, the importance of selecting a proper mud for drilling and interval for testing are explained. Factors affecting gas hydrate formation are discussed. Then, requirements for gas hydrate prevention during DST are described. Actions required to prevent gas hydrate formation during DST startup, fluid sampling, well shut-in and restart are addressed. Finally, procedures for incorporating gas hydrate prevention in DST are outlined.At the end of the paper, a gas well with hypothetical data is used to assist in illustrating the DST procedures.
The main objectives of this paper are to (i) investigate key factors affecting gas hydrate formation and (ii) recommend methods for preventing gas hydrate in deepwater flowing and shut-in gas wells.In this paper, key factors affecting gas hydrate formation in deepwater gas wells are discussed. Critical times when gas and water may contact each other at temperatures below the gas hydrate temperature inside the tubing during flow and shut-in periods are examined. For flowing wells, since gas hydrate formation can easily be detected by monitoring the gas rate, pressure and temperature at the wellhead, gas hydrate prevention can be planned and implemented properly if there is a need. For shut-in wells, since there is no real time data available to determine if gas hydrate formation is taking place, preparations for gas hydrate prevention should be made available at all times, especially for wells which may encounter unplanned shutins during the operations.In order to assist in illustrating the need for gas hydrate prevention under different circumstances, examples using hypothetical data to represent different field or well cases are presented. Also included in this paper is an example which shows results from a gas hydrate study for a deepwater shadow gas well.
The mass spec tra of a se ries of N-aryl , -un sat u rated -lac tams were stud ied. Be sides the mo lec u lar ion, the three char ac ter is tic frag ments such as [M -82] were com monly found in a se ries of N-Aryl , -un sat u rated -lac tams in EI/MS. Fur ther more the mech a nism for the in ter pre ta tion of these fragments is also de scribed. IN TRO DUC TIONOnly a few meth ods for the syn the sis of N-aryl , -unsat u rated -lac tams, a key in ter me di ate for mitomycin, have been re ported in the lit er a ture.1 Un til the pres ent, only a lit tle at ten tion was paid to the EI-MS of these lac tams. 2 A de tailed de scrip tion of these char ac ter is tic fragmental be hav iors is still lack ing. Re cently we have de vel oped a new route for N-aryl , -un sat u rated -lac tams via the ring-closing metath e sis re ac tion. Herein we would like to re port and in ter pret these frag ments by a ra tio nal pro posed mech a nism. This re sult is ex pected to be use ful in for ma tion for fur ther iden ti fi ca tion of N-aryl , -un sat u rated -lac tams. EX PER I MEN TAL SEC TIONThe N-ary , -un sat u rated -lac tams (1a-f) for mass spec tral stud ies were pre pared by our pre vi ously re ported meth ods 3 by treat ing N-aryl 3-(phenylsulfonyl)propanamide with two equiv a lents of tert-butoxide and 1 equiv a lent of allyl bro mide to af ford N-allyl N-aryl alkenamides. The result ing N-allyl N-aryl alkenamides were treated with Grubbs cat a lyst to un dergo the ring-closing me tath e sis (RCM) to give the de sired com pounds, N-aryl , -un sat u rated -lactams. Mass data were re corded on a Hewlett Packard mass spec trom e ter con nected to a Hewlett Packard se ries II model gas-liquid chromatograph. It was equipped with a 12 m 0. RE SULTS AND DIS CUS SIONIn gas chro ma tog ra phy N-aryl , -un sat u rated -lactams both 1a-c and 1d-f showed a reg u lar in crease in re tention time as the mo lec u lar weight in creases. The re ten tion time and char ac ter is tic peaks (m/z ) of N-aryl , -un sat u rated -lac tams in gas chro ma tog ra phy and EI-MS are given in Table 1 .In EI-MS N-aryl , -un sat u rated -lac tams (1a-f) always ex hibit the mo lec u lar ion peak as a base peak, no mat ter what kinds of sub stitu ents ex ist on the ben zene ring. It was also ob served that [M-29] + and [M-82] + ion peaks of 1c have much lower rel a tive in ten sity com pared to those of 1a-b and 1d-f, per haps due to the pres ence of a methoxy group on the para po si tion of the ben zene ring. The rel a tive in ten sity of
Nearly all the decline curve equations used today are based on the Arps hyperbolic equation1. Many engineers prefer to use the exponential decline, which is a special case of the hyperbolic decline, to perform the decline curve analysis because it is easy to apply. Difficulties in using the hyperbolic equation are attributed to the decline rate variation with time and the changing initial rate or time in the production forecast. Reference 2 presents a generalized rate-time hyperbolic equation which can successfully resolve the above difficulties in predicting the future rate. For the reserve estimate, however, the needed rate-cumulative equation is not being discussed. In this paper, a rate-cumulative equation is derived for the reserve estimate. Using the generalized rate-time and rate-cumulative hyperbolic equations, the decline curve analysis can be performed to predict the future rate and estimate the recoverable reserve. These equations are further generalized to accommodate the multiple decline periods which may occur in the production forecast due to field or well optimization. Introduction Since the introduction of the Arps hyperbolic equation1 in 1944, the Arps equation has become a benchmark in the oil industry for performing the decline curve analysis. Although the exponential equation, a special case of the hyperbolic equation, is widely used for production forecasts and reserve estimates, the applicability of the Arps equation is limited by the variation of decline rate with time and/or the changing initial rate or reference time in the prediction phase. Reference 2 presents a generalized ‘rate-time’ (production rate versus time) hyperbolic decline equation to overcome the above difficulties for predicting the future rate. However, the rate-cumulative (production rate versus cumulative production) equation, which is normally needed for the reserve estimate, is not presented. In this paper, a ‘rate-cumulative’ equation is derived based on the generalized rate-time hyperbolic equation. The use of the rate-cumulative equation, together with the rate-time equation, can successfully perform the production forecast and reserve estimate in the decline curve analysis. Procedures for using these equations to predict the future rate and estimate the recoverable reserve are illustrated with an example containing two different decline periods in the production forecast. Rate-Time Hyperbolic Equation and Production Forecast Equation (1) shows the generalized rate-time hyperbolic equation2:Q(t) = Qi × { 1 + b × Di × t }−1/b... (1) where,Di = D0 × (Q0/Qi)-b ... (2) By inserting Equation (2) into Equation (1), the generalized rate-time equation can also be written as:Q(t) = Qi × { 1 + b × [ D0 × (Q0/Qi)-b] × t }−1/b ... (3) In this equation, it allows any point on a hyperbolic decline curve (corresponding to a set of a production rate ‘Qi’ and decline rate ‘Di’) to be used as an initial condition to represent the decline curve. Correlation between any two sets of parameters (say ‘Q0, D0’ and ‘Qi, Di’) that are used as initial conditions to represent the same decline curve is shown in Equation (2). When there is only one set of parameters used in the generalized rate-time hyperbolic equation, by setting Qi=Q0 and Di=D0, Equation (1) becomes the Arps Equation:Q(t) = Q0 × (1 + b × D0 × t)−1/b... (4)
Periodic field measurements and surveys often result in an abundance of data that needs to be analyzed to assist in optimizing the field production. Without a proper approach to managing and interpreting the data, valuable information that may be realized from the data can easily be overlooked. This paper presents the design and application of an automatic production monitoring system that can be set up on a spreadsheet utilizing the spreadsheet's data operation and graphical capabilities. The program can be used as the ‘first pass’ screening tool to evaluate the production performance. Based on the historical production data incorporating the user-specified criteria, the current performance of each well is categorized as ‘normal’, ‘damaged’ or ‘under-performed’. The potential production increases that may be realized by working over candidates in a typical oil field can also be estimated. With the innovative multi-scale plotting and field-wide mapping techniques, the program can provide the reservoir or production engineers with a gross scoping tool for a high-level overview of both individual well behavior and field-scale performance. In this paper, the design considerations of the program, the advantages and disadvantages of its features, and field examples illustrating its applications are discussed. Introduction Advances in computer technology have provided computers with comprehensive graphical and data operation capabilities. These features now allow for the manipulation and analysis of massive amounts of field data; however, this onslaught of information still requires the analytical skills of a trained individual to enable any meaningful conclusions to be made. The situation is complicated by the conundrum that as the volume of available data increases, the more difficult it is to derive its benefits due to the time required for review and evaluation. The objective of the automatic production monitoring system is to provide a program which will replicate the preliminary steps that the typical analyst would take in doing a performance review of an oil field. That is, the system retrieves the data, presents it in a logical and concise manner, and then reviews wells for indications that are performing in a less than optimum manner. Once each well has been reviewed, workover candidates are identified and, where possible, ranked in order of their potential contribution to improvement in oil rates. The analysis is performed automatically and presented to the user for further review and verification. The program also has the capability of presenting commonly collected information such as pressure and injection data in a logical easy-to-view format using multi-scale plotting and field-wide mapping techniques[1]. Therefore, the analyst can also use this tool to develop a more complete understanding of the regional and field-wide performance. This paper presents the design of the automatic production monitoring system, including it's spreadsheet based layout, central control panel, and innovative plotting features. The development of the summary table with its production diagnostic capabilities and ranking hierarchy are discussed. The benefits and concerns inherent with such a tool and its application are contained herein. Program Design – The Automatic Production Monitoring System Overview The automatic production monitoring system is designed for ease of operation, maximum user flexibility, and clarity of visual presentation. It is intended to optimize the skills of the analyst in a familiar PC spreadsheet environment, which has already been demonstrated to be a powerful tool with user-friendly capabilities. The fulfillment of these requirements is described in the following sections which discuss the individual features of the program.
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