Medusa Field in the Gulf of Mexico produces from turbidite sandstone reservoirs at depths between 10,000 and 14,000 feet. Production from the field commenced in December 2003, and six wells are producing at a rate of 40,000 BOE/D. Well A-3 is producing from the T4B reservoir at average oil rates in excess of 10,000 STB/D. The late Miocene T4B reservoir was deposited in a turbidite channel/levee environment with extensive thin-bedded levee and isolated massive sand channel-splay facies. Stratigraphic cross sections and seismic amplitudes show rapid lateral change from levee to channel-splay facies. Depositional slopes, consisting of the east-facing levee flank and west-facing regional slope, and syn-depositional normal faulting control the location and thickness of channel-splay deposition. Conventional core in one T4B well provides information on depositional facies, reservoir properties and rock compaction. The decline of reservoir pressure early in the producing life indicated that well A-3 was connected to a smaller oil volume than estimated from pre-production models. However later performance, the ability to sustain oil flow rate and a slowing of the rate of reservoir pressure decline, suggested communication between the higher quality channel-splay and levee facies and a larger OOIP than indicated by the early performance. The eventual rise in producing gas-oil ratio and the onset of minor water production also affected the well's performance. Simulation history matching commenced using pre-production reservoir characterization models with the objective to forecast well A-3 production and to evaluate the potential for a previously planned second well completion in the zone.Traditional history matching using the original static model was not satisfactory. MEPO®, an optimization tool for assisted history matching, was used to complete the history match study. Multiple equiprobable history matches were obtained varying fault and facies transmissibility, fluid PVT, rock compaction curves, and critical gas saturation. The reserves and production profile risk for a future well completion have been evaluated with a range of the history match results. Medusa Field Description Medusa Field is located in the Gulf of Mexico approximately 110 miles southeast of New Orleans in 2,200 ft of water (Figure 1). The field extends over blocks MC 538 and MC 582 in the Mississippi Canyon area. The discovery well, MC 582 #1OH, reached TD of 16,950 ft MD (15,621 ft TVD SS) in September, 1999. Murphy Oil Exploration and Production Company is operator of the Medusa Field with 60% interest. Field partners include ENI Petroleum Corporation (25%) and Callon Petroleum Operating Company (15%). Subsequent drilling delineated the field and showed that hydrocarbons exist across a major fault/salt-weld structure to the west. Three major reservoir intervals have been identified in addition to several minor reservoir sands. The major productive intervals are the T1B, T4B and T4C. The Medusa Field was developed with the construction of a dry tree SPAR. The production facility has processing capacity for 40,000 STB/D of oil, 110 MM Scf/D of gas and 20,000 STB/D of produced water. Six wells, A-1 through A-6 were initially completed targeting the T1B, T4B and T4C zones. The development plan includes future re-completions and sidetracks to adequately drain all reservoirs. Production from the Medusa Field commenced in December 2003. The late Miocene T4B Sand is one of three major reservoirs in Medusa Field. Other major pay sands include the T1B (Pliocene) Sand and older late Miocene T4C Sand (Figure 2).The T4B Sand occurs at a depth of about 13,000 ft TVD SS and is located east of a salt weld that bisects Medusa Field. The T4B pay sand is located in a structural low and is a stratigraphic trap (Figure 3). Reservoir lithologies consist of thin-bedded sand and shale facies and massive sand. Seismic amplitude and impedance strength delineate the extent of massive sand of the T4B reservoir in this syncline.A single large southwest-dipping normal fault occurs at the west side of the reservoir and several parallel minor faults cut the reservoir sand. An east-west trending, north-dipping fault occurs at the northern end of the massive sand section.
Real Time is the new buzz in the upstream petroleum industry. So far, operators have been the main users of data measured at second or minute time increments to manage wells and keep them on production. Engineers usually see only a sub-set of the data - the daily production volumes and rates along with a few select- gauge pressure and temperature readings. The limited data means that they only see the result - the production volumes - and not the reason for a certain production parameter - e.g. choke size, pressures, temperatures. SCADA - Supervisory Control And Data Acquisition - is the system, which connects to the gauges, collects the measured data and stores it in a database. Operators on the platform have direct access to this data and use them to control the wells. If the engineers see this data at all, they usually get them through a web browser interface and in a format they cannot directly use for their analysis. One of the reasons for this is that many SCADA providers come from other industries where the needs are quite different and the network resources are much more substantial. This paper will introduce a new concept of integrating high frequency data up to the management level. Each level of the organization sees as much as they need or want to see of the high frequency data. The engineers have exactly the same view as the operators at the platform - at the same time. This might seem to be a problem at the first, but in the long term, it is an empowerment of the operators and brings engineers and operators closer together working as a team to manage the wells. The data also allowsmanagement to monitor the oil- and gas production leaving the platform to see if the target volumes are reached, or if a well is shut in. Murphy has implemented this system in all of their operated deepwater assets in the Gulf of Mexico. This paper will give insights on how drastically it transformed the way of doing their daily work, how it changed the way operators work together with engineers, and in addition, an outlook of further improvements to the system based on their experience so far. Note: All values in the figures at the end of the SPE paper are randomly generated and do not represent reality. Introduction Horizontal wells and 4D-seismic have been the last major technological advances in the upstream petroleum industry. Now, it appears, as the so-called intelligent field (also called Smart Field, e-field, i-field, etc.) will be the next major technological advance in the industry. But how is intelligent field defined? Phrases such as closed loop and self-controlled can be read in different publications. This is not what we will focus on in this paper, since a closed loop control still has a long way to go until it becomes reality. We will describe to the reader the basics for an intelligent field, starting with the following questions:What are the main problems in this domainWhat has to be done on the data management sideHow can high frequency data add value to the asset management process As the water depth of newly discovered reservoirs is getting deeper and deeper, the costs of drilling a well sharply increase. A deepwater well can typically cost USD $15MM to $50MM. The facility to support these wells may cost an additional $200–$1,000+MM. These costs have necessitated well performance monitoring to protect these investments. The wells and the platform are equipped with a variety of different sensors, measuring the performance of the wells and the platform's process train's with seconds to minute time increments.
Much of the technology developed in the oil industry today is the result of cooperative engineering research efforts between operating companies with a problem and a technology developer with a potential solution. Often all parties discover the unexpected in the course of making physical measurements. In this case, the data captured showed the advancement in time of the limit singularity associated with a gas/water contact. The purpose of this paper is to share knowledge that may be useful to other operators, particularly those with permanent pressure gauge completions in oil and gas wells, or those operators who may use precision pressure gauges to monitor the flowing tubing pressure of a gas well. The author's companies have engaged in joint reservoir evaluation efforts to resolve rapidly declining production behavior in gas wells. The efforts were based upon pressure transient well evaluations utilizing the capillary shock front theory to map the gas cap at the time of the test. Two examples are presented that illustrate water contact boundary progression just prior to the onset of water production in each of the wells. The joint efforts have resulted in a better understanding of how to use operational shut-ins to monitor gas/water contacts from the inception of flow to the point of water encroachment. The goals of this effort are to see the end coming and perhaps delay the end in order to maximize well production. The secondary goal is to predict the end so as to avoid unnecessary post mortem efforts to repair a well that has watered-out. The first case is a test of a deep well in Louisiana that was being evaluated for rapidly declining pressure and flow rate. The second well was offshore in the Gulf of Mexico that was being evaluated for geology and remaining reserves. The movement of the limit contacts over time is illustrated with a sequential limit mapping presentation. A second test is presented to show an overlay of two tests performed two weeks apart, just before the well watered out. Introduction Since the introduction of the first mechanical pressure gauge, pressure transient data has shown segmentation when plotted on a semi-log plot of pressure vs. log10 t. This led to early observations of specific abrupt changes in slope that were best described as mirror image wells or offset wells that appear to "turn on" when the boundary is contacted by the cone of influence. Often these singularity slope changes were noted as abrupt or "turning on a single data point." This was originally ascribed to friction in mechanical gauges. The advent of accurate electronic pressure gauges eliminated the argument for gauge friction and led to an investigation for other causes.
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