Summary The Genesis project was ChevronTexaco's first Gulf of Mexico deepwater development project and the first industry project to use spar technology for drilling, completion, and production operations.1,2This paper discusses the unique results and lessons learned through the first 4 years of the Genesis project in the areas of integrated teams, organizational learning, spar operations, completion practices, well performance, and reservoir management. Introduction The Genesis field is located in Green Canyon Blocks 160, 161, and 205 in the Gulf of Mexico. The spar is located in Green Canyon Block 205 in 2,590 ft of water. The Genesis project is a joint venture among three partners:56.67% ChevronTexaco (operator), 38.38% ExxonMobil, and 4.95% BHP Billiton. The discovery well was drilled in 1988, and several delineation wells followed in the early 1990s; development funding was approved in 1996.Asemisubmersible rig was used to batch set 36-, 26-, and 20-in. casing strings on 19 wells. The seafloor well pattern includes locations for 20 wells and two export riser bases. Spacing between locations is 20 ft in a140-ft-diameter circle. The semisubmersible rig also drilled and cased the first two development wells. The spar was installed in mid-1998, followed by topside and rig installation. Hookup work began, and the first rig operations—running the export risers—began in November 1998; first production occurred in late January1999. Through the first 4 years of the project, 14 producing wells have been drilled in the Genesis field. These 14 wells include 10 wells with single completions and four wells with stacked wireline-selective completions. Major rig workovers to change producing zones have also been performed on two of the initial 14 wells. Maximum daily production from the Genesis field was 61,130 BOPD and 98,670Mcf/D of gas, achieved on 4 July 2001.Future plans for the field include numerous sidetracks and major rig workovers. Ref. 1 discussed the development plan for the Genesis field as it related to drilling and completion design and execution. This paper presents a unique comparison between the planned and actual development program and describes results and lessons learned through the first 4 years of the Genesis project.

There are on-going efforts in digital transformation in different aspects of hydrocarbon recovery. For well performance surveillance, we have developed the key elements of a Transient Data Surveillance Machine to efficiently process and analyze all transient data from continuous measurements at the wells, allowing for full utilization of the available data. The workflow has been applied at wells in a deep-water oil field in Gulf of Mexico and proved to be effective. We developed Machine Learning (ML) algorithms and techniques to efficiently process and analyze pressure-rate transient data. Following the automatic workflow, K-mean clustering is used to identify shut-in periods, maximum-slope method is used to synchronize pressure and rate data, Supported Vector Machine algorithm combined with Kernel method is used for transient flow-regime recognition, followed by Non-Linear Regression using physical models to estimate reservoir and well properties and assess uncertainty. Through synthetic case and field data testing, we demonstrated that the ML method is tolerant to data noise. Even at 15% of noise level, which is much higher than standard pressure gauge data, the successful rate is 98% in flow-regime identification. However, it is sensitive to data outliers, and we need to include other techniques, such as wavelet data processing, in the workflow. Adding real field data with associated reservoir models that are validated by experts into the training data set could increase the accuracy of pattern recognition 10% more than training with only analytical solutions. The application of our workflow in a deep-water oil field in Gulf of Mexico, which started oil production in 2009 with all wells with permanent downhole pressure gauges, helped to process and analyze transient data from shut-in’s (70% planned transient tests and 30% operation related) efficiently, and derived information about well productivity changes, interference among wells, and permeability reduction due to rock compaction. This enabled continuous well monitoring and effective identification of well productivity issues. The novelty of our Transient Data Surveillance Machine is its capacity in handling huge amounts of dynamic data and its efficiency using real-time data diagnosis for operation decisions and reservoir management.

Summary To manage well productivity, an effort was undertaken to identify fines migration by means of transient diagnosis, quantify its effect on productivity, model the production history, and forecast well performance. Because of its distinguishable transient behavior, mechanical fines migration can be identified among other factors that contribute to productivity decline. Pressure transient analysis (PTA), production data analysis (PDA), laboratory experiments, and numerical-flow-simulation techniques were used to understand the physics of fines migration, quantify its characteristic parameters, validate the model with production history, and verify its efficacy in a field application. Results are consistent with laboratory observations, synthetic studies leveraging a geomechanics reservoir simulator, and field data for moderate to severe fines migration. A new integrated approach was developed to accurately identify and depict declining productivity caused by fines migration through PTA, core testing, and reservoir flow modeling. Previous research has proposed a permeability-reduction flow function that correlates with extended coreflood data to predict the key parameters that characterize the fines-migration effects: critical velocity, permeability-reduction rate, and ultimate residual permeability. From the transient-behavior observations on wells experiencing fines migration, the obvious damage is represented by a positive skin as a function of time in the near-wellbore region. This concurs with the realization that interstitial velocity decreases with the distance from the wellbore. For severe fines migration observed in both synthetic cases and field data, two permeability regions could be identified and described by a radial composite model allowing the damage radius and the average permeabilities of each zone be estimated. Incorporation of a new technique, which correlates the skin-time function with the fines-migration flow relation, enables the calculation of key parameter ranges. These can be integrated with coreflood data for use as initial values in numerical reservoir modeling, potentially simplifying history-matching efforts before performance forecast. The novelty of this workflow is in the ability to identify and quantify the potential influence of mechanical fines migration with PTA and PDA techniques, and incorporation of the fines-migration flow relation to estimate the ranges of the characteristic parameters used in numerical modeling. Understanding the impact of fines migration on well productivity allows engineers to more accurately predict production decline, identify the benefit of remediation, and select optimal development strategies.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThe Genesis project was ChevronTexaco's first Gulf of Mexico deepwater development project and the first industry project to utilize spar technology for drilling, completion, and production operations. This paper discusses the unique results and lessons learned through the first four years of the Genesis project in the areas of integrated teams, organizational learning, spar operations, completion practices, well performance, and reservoir management. SPE 84415 Single Selective Completion SchematicTUBING 3-1/2" 10.2#, HP2 13Cr, 95 ksi 30 deg. TCT CaCl 2 /CaBr 2

Dynamic simulation results of thermal and hydraulic performance of the Gemini deepwater subsea production system of three wells and two pipelines are presented. This paper shows how transient models such as OLGA are used to predict and alleviate the flow assurance problems associated with deepwater production of a gas condensate subsea system. The paper addresses the importance of flow modeling before and during production. The results of this study can be applied to the design of new deepwater production systems in order to maintain flow assurance, and to safely operate a subsea pipeline/riser system. Comparisons of predictions with the measured production data are presented. Simulation ChallengesSteady State and dynamic simulations were performed throughout the conception, design and operation of the Gemini