Vertical Sweep Evaluation in the Lost Hills Diatomite Waterflood Using
Carbon/Oxygen Logs

Badruzzaman, Ahmed; Morea, Micheal; Julander, Dale

doi:10.2523/109608-ms

Proceedings of SPE Annual Technical Conference and Exhibition 2007

DOI: 10.2523/109608-ms

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Vertical Sweep Evaluation in the Lost Hills Diatomite Waterflood Using Carbon/Oxygen Logs

Ahmed Badruzzaman¹,

Micheal Morea²,

Dale Julander³

Abstract: fax 01-972-952-9435. AbstractWe discuss our experience to date with the Carbon/Oxygen logging technique to determine vertical sweep in Belridge Diatomite in the Lost Hills Field. We describe early interpretation challenges with overly optimistic saturation estimation. This required in-house Monte Carlo modeling to understand the tool response in very high porosity reservoirs. A newer vendor algorithm, however, underestimated the oil saturation. In-house test algorithms were then developed with significantly mo… Show more

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Cited by 2 publications

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Infill Well Location Selection Procedures in Lost Hills Using Machine Learning

TaeSoo

Crane

Grijalva

2018

SPE Western Regional Meeting

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The diatomite reservoirs in Lost Hills field provide an interesting reservoir management challenge due to its unique natures such as low permeability (~ 1 md) but high porosity (~ 50%), weak rock strength, and strong imbibition. In most conventional reservoirs, numerical simulation has been successful technology in forecasting, history matching and extraction of valuable information on optimum new well locations placement. However, numerical simulation results of Lost Hills were not successful due to the unique characteristics of the diatomite reservoir noted above. Therefore, the current procedures of selecting new well locations are based on the surface patterns and static reservoir properties. Machine learning (ML) has recently been spotlighted since it does not require specific physical models but can provide good estimations if there is enough data. Features like permeability, oil saturation, and other common reservoir properties also impact on production in diatomite reservoir, but the contribution of each property is not clearly understood. Because of its characteristics of not requiring physical models, ML has strong applicability in Lost Hills of which exact hydrocarbon production mechanisms are not clearly understood. In this study, a new methodology using Machine Learning was introduced for infill well location selection. This approach led to identification of approximate 550 infill well candidate locations, which were recommended further to the asset development team for execution. The infill well location selection workflow consists of historic data analysis, drainage area estimation, ML model training, and candidate location estimation. Using K-means clustering and statistical means, the characteristics of good producers are analyzed. Then, the drainage area for each well is approximated using Voronoi cells. This allows a relative good estimation of the remaining oil in place and non-draining areas. A support vector machine (SVM) model was trained to rank the locations based on their production capabilities. The new workflow provides more systematic means on new well location selection and enable the asset team to make data driven decisions.

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Infill Well Location Selection Procedures in Lost Hills Using Machine Learning

TaeSoo

Crane

Grijalva

2018

SPE Western Regional Meeting

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Improvement of Reservoir Management Efficiency Using Stochastic Capacitance Resistance Model

Kim

2019

SPE Western Regional Meeting

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Lost Hills is a dual permeability Diatomite reservoir that is distinct from conventional reservoirs. Application of numerical simulation has been limited throughout field life due to the complex nature of the diatomite including low permeability (∼ 1 md) but high porosity (∼ 50%) and weak rock strength (∼ 100,000 psi of Young's modulus). Thus, many reservoir management practices are based on trial and error methods which are sub-optimal. This work aims to enhance the efficiency of reservoir management activities. The usage of Capacitance Resistance Model (CRM) has been increasing due to its simple but effective capabilities of analyzing waterflooding performances. However, CRM has innate limitations as well. The core calculation of CRM is solving nonlinear regression. Solution of nonlinear regression is not unique since it is not guaranteed to find the global minimum and is affected by solver algorithms and initial guesses. In addition to the innate limitations, due to the lack of bottomhole pressure data in the Lost Hills and its high oil viscosity (∼ 20 °API), the accuracy of Lost Hills CRM solution is not enough to be used in daily operations. Stochastic CRM (SCRM) was developed to mitigate these limitations by combining bootstrap with CRM and provides stochastic answers. SCRM estimates probabilities of an initial solution using bootstrap, substitutes low probable parameter values with P50 values, and updates injector-producer connection pairs and its interwell connectivity. SCRM was developed for analyzing waterflooding operations such as identification of ineffective injector-producer connection pairs and estimation of reservoir pressure. SCRM analysis results were benchmarked against the Lost Hills tracer test data and demonstrated that SCRM provided a better solution than CRM. Compared to the fact that the deterministic solution from CRM found only 50% of connection pairs which tracer identified, SCRM solution identified 10 out of the 12 tracer identified connections. After the verification, SCRM was applied to find out connected injectors which cause Fluid Over Pump (FOP) wells. The existing workflow for identifying connected injectors was a trial and error method and hard to find connected injectors if connected injectors are located farther than 300 ft from FOP wells (chronic FOP wells). The novel workflow has been deployed so that FOP wells can be mitigated systematically and enable the optimization team to improve its reservoir management efficiency. In 2017, 10 chronic FOP wells were mitigated by identifying connected injectors with the novel workflow.

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Vertical Sweep Evaluation in the Lost Hills Diatomite Waterflood Using Carbon/Oxygen Logs

Cited by 2 publications

References 7 publications

Infill Well Location Selection Procedures in Lost Hills Using Machine Learning

Infill Well Location Selection Procedures in Lost Hills Using Machine Learning

Improvement of Reservoir Management Efficiency Using Stochastic Capacitance Resistance Model

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